Abstract To date, machine learning models have been widely applied to intrusion detection system (IDS) for improving detection accuracy, where most IDS suffer from adversarial evasion attacks that may lead to data loss and user privacy leakage. Although there have been numerous solutions proposed against adversarial evasion attacks, they often neglect the relationships between different traffic and heavily relied on data labels. Therefore, this paper proposes AEDGNN, a new approach for detecting adversarial evasion attacks using graph neural network (GNN) model. On one hand, AEDGNN employs E-GraphSAGE to capture network topology in IDS for building the relationship between different inputs. On the other hand, AEDGNN utilizes deep graph infomax (DGI) to train the GNN in a self-supervised manner for maximizing mutual information between local and global representations. In addition, to clarify the practical performance of defending against traditional adversarial attacks, we implement AEDGNN and classic machine learning models based on CIC-IDS2018 benchmark dataset. The experimental results show that AEDGNN achieves significant improvements on both normal and adversarial samples compared to classic solutions. The accuracy of AEDGNN is 0.02%–1.53% higher than that of classic solutions for normal samples, and 26.04%–59.04% higher for adversarial samples.

The digital transformation of healthcare necessitates advanced mechanisms for secure, privacy-preserving data sharing. Traditional methods struggle with the complexities of modern application scenarios such as cross-domain collaboration. We propose a privacy-preserving medical healthcare intelligent system built upon a novel Distributed Dynamic Threshold Attribute-based Encryption (DDTABE) scheme. Our DDTABE scheme is proven to achieve IND-CPA security under the aMSE-DDH hardness assumption in the random oracle model. The resulting system facilitates secure cross-domain medical data sharing and allows medical personnel to join dynamically for flexible collaboration. A key feature is its robustness as it resists unauthorized attributes through a verifiable mechanism. Performance analysis and experimental comparisons demonstrate the efficiency of our approach. Specifically, our scheme exhibits significant advantages in the running times of the Setup, Encrypt, and Decrypt algorithms compared to existing work, as the sizes of attribute sets in keys and policies grow, while maintaining constant ciphertext complexity.

This paper investigates robust and secure active intelligent reflecting surface (IRS)-aided covert multi-user multiple-input multiple-output (MIMO) communications in the existence of a multi-antenna energy-harvesting eavesdropper. In particular, an active IRS is deployed to establish a favorable wireless communication environment for improving both security and covertness of the system. We aim to maximize the total system secrecy rate by jointly optimizing the transmit beamforming and artificial noise matrices at the base station (BS) and the reflection-coefficients of the active IRS. Furthermore, we formulate the design as a non-convex optimization problem, explicitly taking into account energy harvesting capabilities of the eavesdropper, information security requirements of legitimate users, and covertness constraint against adversarial detection. To address the inherent non-convexity of the optimization design problem, we propose an effective iterative suboptimal algorithm. In particular, we first transform the original optimization problem into a tractable form by employing the weighted minimum mean-square error (WMMSE), general sign-definiteness, and S-Procedure methods. Subsequently, we exploit the block coordinate descent (BCD) approach to decompose the coupled optimization matrices. Simulation results demonstrate that the proposed scheme can substantially outperform various baseline schemes adopting existing solutions. Besides, our results highlight the superiority of integrating active IRS into MIMO communication systems for secure and covert transmission.

Self-orthogonal codes are an important type of linear codes since they are very closely related to designs, lattices, and quantum codes. Bose-Chaudhuri-Hocquenghem codes (BCH codes) have various practical applications in communication and storage due to their efficient encoding and decoding algorithms. In this paper, we will focus on the primitive and projective self-orthogonal BCH codes in both Euclidean and Hermitian cases. Our main objective is to characterize primitive and projective Euclidean and Hermitian self-orthogonal BCH codes and investigate their parameters. For the Euclidean case, the primitive and projective self-orthogonal BCH codes are characterized completely by using their designed distances. For the Hermitian case, a sufficient and necessary condition for all primitive BCH codes being self-orthogonal are presented, while the characterizations of projective Hermitian self-orthogonal BCH codes are obtained in some cases. Moreover, the dimensions of some Euclidean and Hermitian self-orthogonal BCH codes are determined explicitly and lower bounds on their minimum distances are given.

Functional encryption (FE) which covers the notion of attribute-based encryption (ABE), is the cryptographic tool to realize fine-grained control on the accessibility of encrypted data. The traditional FE requires a central trusted authority to issue secret keys. It depends on the full-trust model, and is vulnerable to the security issue caused by key-escrow. While the registered FE (Reg-FE) achieves the zero-trust model and addresses the security issue by removing the use of central authority. It allows users to generate secret keys themselves and join the system by registering corresponding public keys to a curator. This work introduces delegated Reg-FE, which is a primitive with a new registration paradigm. It allows the registration of certain authorities that can issue secret keys for their respective classical FE sub-systems, beyond the prior work of registering plain users. Delegated Reg-FE implements a hybrid trust model within a two-level hierarchy. By redefining key escrow as a functional mechanism rather than a security concern, this model employs a zero-trust upper level which removes key-escrow, while the subsystem of each authority is locally full-trust and retains key-escrow mechanism. We construct four delegated Reg-FE schemes for functionalities that can be described as the $$2\times 2$$ combinations of linear function and policy check. Namely, Delegated Reg-IPFE, Delegated Reg-ABE, Reg-IPFE with delegated ABE, and Reg-ABE with delegated IPFE. All concrete schemes support bounded registrations and delegations, and achieve standard adaptive security under $$\textsc {MDDH}$$ assumption on prime-order bilinear group. Furthermore, these schemes only rely on black-box techniques. Technically, these schemes relies on dual-system techniques as prior registration-based works. And we devise a new “hierarchically invoked dual-system” technique on schemes which have sub-ABE delegation systems. Furthermore, we present a generic construction of Delegated Reg-FE from the combination of Reg-FE and FE. The instantiations of this generic construction demonstrate the feasibility of delegated Reg-FE, supporting arbitrary functions as well as unbounded numbers of registrations and delegations. However, this approach requires non-black-box techniques and achieves weaker semi-adaptive security without malicious registration, where the semi-adaptive means the adversary claims the challenge after seeing common reference string but before making any query. Its security relies solely on the underlying assumptions of the Reg-FE and FE components.

Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from various clients. Although FGL allows client data to remain localized, a malicious server can still steal client private data information through uploaded gradient. In this paper, we for the first time propose label distribution attacks (LDAs11The term “LDA” here is different from other machine learning terms like Latent Dirichlet Allocation.) on FGL that aim to infer the label distributions of the client-side data. Firstly, we observe that the effectiveness of LDA is closely related to the variance of node embeddings in GNNs. Next, we analyze the relation between them and propose a new attack named ECLDA, which significantly improves the attack effectiveness by compressing node embeddings. Then, extensive experiments on node classification and link prediction tasks across six widely used graph datasets show that EC-LDA outperforms the SOTA LDAs. Specifically, EC-LDA can achieve the Cos-sim as high as 1.0 under almost all cases. Finally, we explore the robustness of EC-LDA under differential privacy protection and discuss the potential effective defense methods to EC-LDA. Our code is available at https://github.com/cheng-t/EC-LDA.

Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

It is common to reject undesired outputs of Large Language Models (LLMs); however, current methods to do so require an excessive amount of computation to re-sample after a rejection, or distort the distribution of outputs by constraining the output to highly improbable tokens. We present a method, Approximately Aligned Decoding (AprAD), to balance the distortion of the output distribution with computational efficiency, inspired by algorithms from the speculative decoding literature. AprAD allows for the generation of long sequences of text with difficult-to-satisfy constraints, while amplifying low probability outputs much less compared to existing methods. We show through a series of experiments that the task-specific performance of AprAD is comparable to methods that do not distort the output distribution, while being much more computationally efficient.

Abstract Inner product arguments are core building blocks of numerous cryptographic primitives and therefore minimizing their complexity is a central goal in this research area. In this paper, we follow the work of Kim et al. (ASIACRYPT’22) and propose the first inner product argument having sublogarithmic communication complexity and sub-square-root verifier complexity simultaneously. We first devise a new subvector combination method for recursion and utilize an aggregated multi-exponentiation argument to prove some committed group elements are valid. We then modify the commitment keys in inner product arguments to be structured and reduce the verifier complexity by delegating the costly computations to the prover. Compared with the state-of-the-art inner product arguments, our protocol is highly competitive in terms of asymptotic complexity.

Outsourcing personal data to a convenient and affordable cloud platform has become a popular practice. Considering the risk of privacy leakage, users usually encrypt their data before uploading it to the cloud server. Searchable encryption (SE) allows cloud servers to manage and search data in encrypted form based on user-specified requests. However, coercion attacks are rarely considered, where users may be forced to open search records and results. Therefore, deniable SE solutions against coercion attacks are presented, but they suffer from large storage overhead or fail to consider the dual coercion situation towards both sides of data owners and data users. In this paper, we roughly combine oblivious cross-tags protocol (OXT) and deniable encryption to propose a deniable SE (deniable cross-tag, DXT) scheme, which supports boolean queries and resists dual coercion attacks. Technically, we formalize a new primitive called updatable deniable encryption, and combine it with OXT in a non-trivial manner. In addition, we give formal system model, security model, and security proof of DXT. By employing the HUAWEI cloud platform, we conduct sufficient comparative experiments between DXT and state-of-the-art solutions based on a public dataset. The experimental results demonstrate that DXT outperforms higher search efficiency while achieving better features.

Data sharing technology plays an important role in sharing information on mobile devices, ensuring that users can preserve their privacy while guaranteeing secure data transmission. Matchmaking encryption is a novel cryptographic primitive that provides bilateral access control to maintain user trust and data integrity. However, this primitive faces a challenge in terms of achieving secure multi-receiver construction. In a multi-user environment, users need to encrypt the data many times, resulting in inefficiencies under this approach. To address this challenge, we focus on the underlying construction of identity-based broadcast matchmaking encryption (IBBME). This paper presents a new IBBME construction with DBDH andq-SDH assumptions under the standard model. Specifically, we propose a new approach that abandons the generalized transformations that already existed previously in multiple receivers. Specifically, we adopt the “two-level” method to guarantee privacy and authenticity, where the identity-based broadcast encryption (IBBE) level guarantees privacy, while the signature level guarantees authenticity. In addition, we present a strict security proof, which shows that our construction satisfies privacy and authenticity exactly. Moreover, we compare the existing ME constructions with our construction through theoretical and performance analysis. The analysis shows that the ciphertext size in our construction can be reduced to be independent of the number of receivers, which is more efficient.

Federated Learning (FL) is a distributed machine learning framework that allows multiple clients to collaboratively train an intermediate model with keeping data local, however, sensitive information may be still inferred during exchanging local models. Although homomorphic encryption and multi-party computation are applied into FL solutions to mitigate such privacy risks, they lead to costly communication overhead and long training time. As a result, functional encryption (FE) is introduced into the field of privacy-preserving FL (PPFL) for boosting efficiency and enhancing security. Nevertheless, existing FE-based PPFL frameworks that support dynamic participation either required a trusted third party that may lead to single-point failure, or require multiple rounds of interaction that inevitably incur large communication overhead. Therefore, we propose PrivLDFL, a lightweight and dynamic PPFL framework for resource-constrained devices. Technically, we formalize dynamic decentralized multi-client FE and give instantiations, then present efficiency optimizations via designing a vector compression funnel based on Chinese Remainder Theorem, and finally achieve client dropouts via a client partitioning strategy. Besides formal security analysis on PrivLDFL, we implement it and state-of-the-art solutions on Raspberry Pi to conduct extensive experiments, confirming the practical performance of PrivLDFL on best-known public datasets.

Secure cloud storage is a prevalent way to provide data retrieval services, where users’ data are encrypted before uploading to the cloud. To effectively perform keyword searches over the encrypted data, the approach of searchable encryption (SE) was introduced. However, the leakage of the keyword-pair result pattern to the cloud could be exploited to reconstruct the queried keywords. To mitigate such information leakages, numerous result pattern-hiding SE systems were proposed but rarely supported data sharing with expressive queries and even owner-enforced authorization. Therefore, we present a result pattern hiding and authorized SE system (AXT) supporting conjunctive queries for cloud-based data sharing. Technically, we construct an authorized label private set intersection protocol from a refined authorized public key encryption with an equality test and then combine it with an introduced asymmetric variant of oblivious cross-tag protocol. Moreover, we introduce the system and security model of AXT along with rigorous security proof. Furthermore, we conduct comparative experiments between state-of-the-art solutions with AXT on HUAWEI Cloud platform under the widely recognized Enron dataset, which reveal that AXT achieves practical performance with retaining authorized data sharing and result pattern hiding, specifically, the time overhead for conjunctive queries with 10 keywords is reduced by 20$\%$.

In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts. This approach aims to reduce redundant memory IO costs, a significant factor in latency for high batch sizes and long context lengths. Bifurcated attention achieves this by dividing the attention mechanism during incremental decoding into two distinct GEMM operations, focusing on the KV cache from prefill and the decoding process. This method ensures precise computation and maintains the usual computational load (FLOPs) of standard attention mechanisms, but with reduced memory IO. Bifurcated attention is also compatible with multi-query attention mechanism known for reduced memory IO for KV cache, further enabling higher batch size and context length. The resulting efficiency leads to lower latency, improving suitability for real-time applications, e.g., enabling massively-parallel answer generation without substantially increasing latency, enhancing performance when integrated with post-processing techniques such as reranking.

In this paper, a secure wireless communication system, where a multi-antenna access point (AP) sends confidential information to a single-antenna user with the help of an untrusted relay adopting amplify-and-forward (AF) protocol and an IRS, is investigated. It is assumed that the link between the IRS and the untrusted relay is present. A secrecy rate maximization problem subject to the resource constraints at the AP and the untrusted relay is formulated. Then, an alternating iteration algorithm jointly optimizing the transmit beamforming matrix at the AP, the phase shifts of the IRS in two time slots and the relay beamforming matrix is proposed. Afterwards, the asymptotic expressions for the maximum secrecy rates of the proposed scheme in the high and low transmitted signal-to-noise ratio (SNR) regimes are derived as well. Finally, numerical evaluations demonstrate the superiority of the proposed scheme compared with other benchmark schemes, highlight the importance of properly designed phase shifts at the IRS, and validate the theoretical analysis.

This article presents a novel approach to the safety verification of hybrid systems by synthesizing neural barrier certificates (BCs) via counterexample-guided neural network (NN) learning combined with sum-of-square (SOS)-based verification. We learn more easily verifiable BCs with NN polynomial expansions in a high-accuracy counterexamples guided framework. By leveraging the polynomial candidates yielded from the learning phase, we reformulate the identification of real BCs as convex linear matrix inequality (LMI) feasibility testing problems, instead of directly solving the inherently NP-hard nonconvex bilinear matrix inequality (BMI) problems associated with SOS-based BC generation. Furthermore, we decompose the large SOS verification programming into several manageable subprogrammings. Benefiting from the efficiency and scalability advantages, our approach can synthesize BCs not amenable to existing methods and handle more general hybrid systems.

In cross-device federated learning, verifiable secure aggregation enables clients to aggregate their locally trained model parameters through a malicious server to obtain a global model. To prevent the malicious server from tampering with the results, solutions have been proposed to ensure verifiability during this aggregation process. However, previous solutions either failed to achieve non-interactivity, which is critical in the cross-device setting, or failed to achieve malicious server resistance due to their underlying approach. Thus in this paper, we propose a lightweight non-interactive multi-client verifiable computation scheme (lwMVC) and then construct the malicious-resistant non-interactive verifiable aggregation (MRNIVA) for cross-device federated learning by the newly introduced underlying approach lwMVC. By combining Half Gates and non-iterative proxy oblivious-transfer, lwMVC meets both the non-interactivity and malicious resistance properties. Then, after solving the user dropout and efficiency problem, we construct the MRNIVA by lwMVC. Since we focus on the cross-device scenario, we conduct experiments on a Single Board Computer. The experiment result demonstrates that MRNIVA remains efficient compared to previous works after achieving both non-interactive and malicious resistance properties. As far as we know, we are the first to carry out verifiable aggregation experiments using such resource-constrained devices.

Electronic healthcare (E-health) cloud system enables electronic health records (EHRs) sharing and improves efficiency of diagnosis and treatment. In order to address EHRs confidentiality and authorized user access control in E-health cloud, attribute-based proxy re-encryption (ABPRE) has been widely employed which provides dynamic fine-grained access control over encrypted EHRs. Unfortunately, existing ABPRE schemes still have the following defects: 1) capacity of attribute-universe is defined at setup; 2) verifiable mechanism for re-encryption reveals EHRs about patients; 3) traditional access policy reveals sensitive information pertaining to patients. This paper focuses on these issues and presents large-universe, verifiable and privacy-preserving dynamic fine-grained access control scheme for E-health cloud. More details, we solve limitation of attribute-universe to large-universe, which means that attributes aren’t required to be enumerated at setup. Considering disclosure of underlying EHRs in verifiable mechanism, scheme introduces non-interactive zero-knowledge proof as verifiable mechanism that supports public validation and doesn’t leak EHRs of patients. Furthermore, partially hidden policy is employed to protect privacy of patients in policy, which divides attribute into attribute name and attribute value, displaying attribute name and hiding attribute value. Finally, experimental evaluation is given that demonstrates the more comprehensive functionality of our scheme without sacrificing significant computational overhead.

The (Schur) squares of linear codes are an interesting research topic in coding theory, and they have important applications in cryptography. Linear complementary dual codes (LCD codes) have been widely applied in data storage, communication systems, consumer electronics, and cryptography. Given these exciting applications of squares and LCD codes, we mainly focus on the squares of LCD cyclic codes in this paper. It will be proved that the square of an LCD cyclic code is still an LCD cyclic code. As a subclass of cyclic codes, Bose-Chaudhuri-Hocquenghem codes (BCH codes) have explicit defining sets that include consecutive integers, which gives an advantage of analyzing the parameters of BCH codes and their related codes. We will investigate the squares$\mathcal {C}^{2}(t)$and$\mathcal {C}^{2}(t)^{c}$of the primitive LCD BCH codes$\mathcal {C}(t)$and their complements$\mathcal {C}(t)^{c}$, respectively, where$\mathcal {C}(t)=\mathcal {C}_{(q,q^{m}-1,2t,-t+1)}$is the BCH code of length$q^{m}-1$over$\mathbb{F}_{q}$with designed distance$2t$. Two sufficient and necessary conditions to guarantee that$\mathcal {C}^{2}(t) \ne \Bbb \{\textbf {0}\}$and$\mathcal {C}^{2}(t)^{c} \ne \mathbb{F}_{q}^{n}$are proposed by giving restrictions on designed distances. Furthermore, the dimensions and lower bounds on minimum distances of$\mathcal {C}^{2}(t)$and$\mathcal {C}^{2}(t)^{c}$are presented in some cases. The parameters of the squares of the complements of the Melas codes$M(q,m)$are also investigated.

Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

The Logical Credal Network or LCN is a recent probabilistic logic designed for effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN specifies a set of probability distributions over all interpretations of a set of logical formulas for which marginal and conditional probability bounds on their truth values are known. Inference in LCNs involves the exact solution of a non-convex non-linear program defined over an exponentially large number of non-negative real valued variables and, therefore, is limited to relatively small problems. In this paper, we present ARIEL -- a novel iterative message-passing scheme for approximate inference in LCNs. Inspired by classical belief propagation for graphical models, our method propagates messages that involve solving considerably smaller local non-linear programs. Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered.

ML-powered code generation aims to assist developers to write code in a more productive manner by intelligently generating code blocks based on natural language prompts. Recently, large pretrained deep learning models have pushed the boundary of code generation and achieved impressive performance. However, the huge number of model parameters poses a significant challenge to their adoption in a typical software development environment, where a developer might use a standard laptop or mid-size server to develop code. Such large models cost significant resources in terms of memory, latency, dollars, as well as carbon footprint.

Summary As the new generation of power scheme, smart grid is proposed to overcome the shortcomings of traditional systems, such as low efficiency and reliability. In this article, a novel lattice‐based secure primitive for privacy‐preserving smart grid communications is proposed, which has the remarkable characteristics, such as scalable multi‐dimensional fine‐grained power data structure and differential privacy security. First, combining with the lattice‐based data encryption technology, while effectively resisting quantum attacks, the method of simultaneous processing of multi‐dimensional data is innovated. Second, through combining the additive homomorphism of the lattice‐based cryptosystem and the Chinese remainder theorem, the data aggregation mechanism that can directly perform homomorphic operations on compressed ciphertext is constructed. Thanks to the above innovative design ideas, the proposed scheme not only significantly improves the efficiency of data communication and processing, greatly reduces the computational cost of the intermediate entity, but also realizes the data confidentiality and information privacy. Finally, observing the decentralized topology of communication nodes in the typical cyber‐physical system of smart grid, the localized differential privacy technology is leveraged to optimize and balance the utility, security, and efficiency of differential privacy. Extensive performance evaluations are conducted to illustrate that the proposed scheme outperforms the state‐of‐the‐art similar schemes in terms of computation complexity and communication cost.

We present an inner‐product matchmaking encryption (IP‐ME) scheme achieving weak privacy and authenticity in prime‐order groups under symmetric external Diffie–Hellman (SXDH) assumption in the standard model. We further present an IP‐ME with Monotone Span Program Authenticity (IP‐ME with MSP Auth) scheme, where the chosen sender policy is upgraded to MSP, and the scheme also achieves weak privacy and authenticity in prime‐order groups under SXDH assumption in the standard model. Both of the schemes have more expressive functionalities than identity‐based matchmaking encryption (IB‐ME) scheme, and are simpler than Ateniese et al.’s modular ME scheme (Crypto’ 19). But our schemes only achieve a very limited flavor of security, which is reflected in the privacy.

Abstract Identity‐based Matchmaking Encryption (IB‐ME) is an extension notion of matchmaking encryption (CRYPTO 2019), where a sender and a receiver can specify an access policy for the other party. In IB‐ME, data encryption is performed by not only a receiver identity but also a sender's encryption key. Nevertheless, previous IB‐ME schemes have not considered the problem of efficient revocation . Hence, the authors introduce a new notion of revocable IB‐ME (RIB‐ME) and formalise the syntax and security model of RIB‐ME. In particular, the authors give an effective and simple construction of RIB‐ME in the standard model, whose security is reduced to the hardness of decisional bilinear Diffie—Hellman problem and computational Diffie—Hellman problem. In addition, the authors show two extensions of our RIB‐ME scheme to consider chosen‐ciphertext security and forward privacy.

Federated Learning (FL), emerging as a distributed machine learning, is a popular paradigm that allows multiple users to collaboratively train a intermediate model by exchanging local models without the training data leaving each user’s domain. However, FL still suffer from privacy risk such as leaking private information from users’ uploaded local models. To address the privacy concern, several approaches have been proposed to achieve privacy-preserving FL (PPFL) based on differential privacy (DP), multi-party computation (MPC), homomorphic encryption (HE) and functional encryption (FE). Compared with DP, MPC and HE, approaches based on FE is more advantageous and thus becomes the focus of this work. Moreover, all existing PPFL schemes via FE employ a multi-user extension of FE for a specific function, i.e., multi-input FE (MIFE). In this paper, we point out that existing FE-based PPFL schemes have faced with several security issues due to the misuse of MIFE. After reconsidering the security requirements of PPFL, we propose new goals of designing PPFL using FE. To achieve our goals, we propose a new FE called dual-mode decentralized multi-client FE (2DMCFE) and give a concreate construction for 2DMCFE. With 2DMCFE, we propose a new framework of PPFL where we establish a fresh 2DMCFE instance for each subset of users. Security proof shows the strong security of our framework under the semi-honest security setting. Furthermore, experiments conducted on real dataset demonstrate that our framework achieves comparable model accuracy and training efficiency to the basic FE-based scheme while providing stronger security guarantee.

Studying the Schur square of a linear code is an important research topic in coding theory. Schur squares have important applications in cryptography and private information retrieval schemes, notably in secure multiparty computing or designing bilinear multiplication algorithms in finite extensions of finite fields through the notion of supercodes. Thanks to their exciting applications in cryptography, squares and powers of several linear codes have been investigated. In this paper, we will focus on the Schur square of a relevant well-known subclass of cyclic codes, Bose-Chaudhuri-Hocquenghem codes (BCH codes), which have wide applications in communication and storage systems and benefit from explicit defining sets that include consecutive integers, which gives the advantage of analyzing the parameters of BCH codes and their complements. Our main objective is to investigate the parameters of the squares of primitive narrow-sense BCH codes$\mathcal C(\delta)$and their complements$\mathcal C(\delta)^{c}$. We will present two sufficient and necessary conditions to guarantee that$\mathcal C^{2}(\delta) \ne \Bbb F_{q}^{n}$and$\mathcal C^{2}(\delta)^{c} \ne \Bbb F_{q}^{n}$by giving restrictions on designed distance$\delta $, where$2 \le \delta \le n$. Based on these two characterizations, the dimensions and minimum distances of$\mathcal C^{2}(\delta)$and$\mathcal C^{2}(\delta)^{c}$are investigated in some cases. The dimensions of these squares are determined explicitly, and lower bounds on the minimum distance are given.

Blockchain, a specific distributed database which maintains a list of data records against tampering and corruption, has aroused wide interests and become a hot topic in the real world. Nevertheless, the increasingly heavy storage consumption brought by the full-replication data storage mechanism, becomes a bottleneck to the system scalability. To address this problem, a reliable storage scheme named BFT-Store (Qiet al.2020), integrating erasure coding with Byzantine Fault Tolerance (BFT), was proposed recently. While, three critical problems are still left open: (i) The complex re-initialization process of the blockchain when the number of nodes varies; (ii) The high computational overload of downloading data; (iii) The massive communication on the network. This paper proposes a better trade-off for blockchain storage scheme termed PartitionChain which addresses the above three problems, maintaining the merits of BFT-Store. First, our scheme allows the original nodes to merely update a single aggregate signature (e.g., 320 bits) when the number of nodes varies. Using aggregate signatures as the proof of the encoded data not only saves the storage costs but also gets rid of the trusted third party. Second, the computational complexity of retrieving data by decoding, compared to BFT-Store, is greatly reduced by about$2^{18}$times on each node. Third, the amount of transmitted data for recovering each block is reduced from$O(n)$(assuming$n$is the number of nodes) to$O(1)$, by partitioning each block into smaller pieces and applying Reed-Solomon coding to each block. Furthermore, this paper also introduces a reputation ranking system where the malicious behaviors of the nodes can be detected and marked, enabling PartitionChain to check the credits of each node termly and expel the nodes with misbehavior to the specific extent. Comparing with BFT-Store, our scheme allows blockchain system to suit dynamic network with higher efficiency and scalability.

In this paper, an intelligent reflecting surface (IRS) assisted wireless secure communication system is studied. A multi-antenna access point (AP) intends to send confidential information to a single-antenna user in the presence of an amplify-and-forward (AF) untrusted relay, which may eavesdrop the message when helping relay the signal. It is assumed that the direct link between the AP and the user is blocked by the obstacles. The achievable secrecy rate maximization problem is then formulated. To overcome the non-convexity of the formulated problem, an alternating iteration algorithm is proposed to jointly optimize the active and passive beamforming. Specifically, the transmit beamforming vector at the AP, the phase shift matrix at the IRS, and the relay beamforming matrix are jointly optimized to maximize the secrecy rate. Moreover, the asymptotic expressions for the maximum secrecy rates of the proposed scheme in the high and low transmitted signal-to-noise ratio (SNR) regimes are derived as well. Finally, numerical evaluations demonstrate the superiority of the proposed scheme compared with other benchmark schemes, highlight the importance of properly designed phase shifts at the IRS, and validate the theoretical analysis. It is also demonstrated that the number of antennas at the AP should be strictly larger than the number of antennas at the relay to harvest the benefits of increasing the transmit power at the AP in increasing the secrecy rate.

We introduce Logical Credal Networks (or LCNs for short) -- an expressive probabilistic logic that generalizes prior formalisms that combine logic and probability. Given imprecise information represented by probability bounds and conditional probability bounds on logic formulas, an LCN specifies a set of probability distributions over all its interpretations. Our approach allows propositional and first-order logic formulas with few restrictions, e.g., without requiring acyclicity. We also define a generalized Markov condition that allows us to identify implicit independence relations between atomic formulas. We evaluate our method on benchmark problems such as random networks, Mastermind games with uncertainty and credit card fraud detection. Our results show that the LCN outperforms existing approaches; its advantage lies in aggregating multiple sources of imprecise information.

Abstract Counter-cryptanalysis uses cryptanalytic techniques to detect cryptanalytic attacks. It was introduced by Stevens with a collision detection algorithm that detects whether a message is one of a colliding message pair constructed using a collision attack. Later, Stevens and Shumow improved the collision detection against SHA-1 by using unavoidable conditions. However, there are no results improving collision detection against MD5 due to its weak diffusion properties. In this paper, an improved collision detection algorithm against MD5 is proposed by using the 14-bit sufficient condition combinations. This leads to the dividing the 223 classes into four sets. Each element, belonging to the first two sets, holds the same sufficient condition combination. Our new algorithm can classify 126 classes efficiently. The runtime is 28.6% of the previous collision detection method.

Cloud storage has become a primary industry in remote data management service but also attracts security concerns, where the best available approach for preventing data disclosure is encryption. Among them the public key encryption with keyword search (PKSE) is considered to be a promising technique, since clients can efficiently search over encrypted data files. That is, a client first generates a search token when to query data files, the cloud server uses the search token to proceed the query over encrypted data files. However, a serious attack is raised when PKSE meets cloud. Formally speaking, the cloud server can learn the information of a newly added encrypted data file containing the keyword that previously queried by using the search tokens it has received, and can further discover the privacy information. To address this issue, we propose a forward secure public key searchable encryption scheme, in which a cloud server cannot learn any information about a newly added encrypted data file containing the keyword that previously queried. To better understand the design principle, we introduce a framework for constructing forward secure public key searchable encryption schemes based on attribute-based searchable encryption. Finally, the experiments show our scheme is efficient.

This paper considers secure communication in buffer-aided cooperative wireless networks in the presence of one eavesdropper, which can intercept the data transmission from both the source and relay nodes. A new max-ratio relaying protocol is proposed, in which different relays are chosen for reception and transmission according to the ratio of the legitimate channels to the eavesdropper channels, so that the relay selected for reception and the relay selected for transmission can receive and transmit at the same time. It is worth noting that the relay employs a randomize-and-forward (RF) strategy such that the eavesdropper can only decode the signals received in the two hops independently. Theoretical analysis of the secrecy throughput of the proposed scheme is provided and the approximate closed-form expressions are derived, which are verified by simulations. Through numerical results, it is shown that the proposed scheme achieves a significant improvement in secrecy throughput compared with existing relay selection policies.

The paper goes into Bitcoin PoW (Proof-of-Work) consensus, especially the variances of inter-block time and of mining reward which may turn out to be the origin of Bitcoin's fundamental weakness (e.g., unexpected centralization and double-spend attack). We procure a contribution-and-gain solution - LVRT, a collaborative PoW consensus with low variances of mining reward and of inter-block time, to intensify mining transparency and incentivize collaborative effort. More precisely, LVRT looses the solutions of cryptographic puzzles by introducing tweakable factor to difficulty target and exploits all qualified computing powers (rather than throws them all away except the first one that wins the puzzle, as handled in Bitcoin) to generate the block (all qualified miners would thereby be rewarded). Low variance of inter-block time also stems from our strategy in packing the block that$k$(a pre-defined system parameter to characterize collaborative effort) smallest out of all qualified hash values are determined and the corresponding average is bounded by difficulty target as well. We define particular block structure to fit our requirements. LVRT is supported by theoretical analysis on the relationship between collaborative effort and the variances of inter-block time and mining reward. Experiments further demonstrate LVRT's resistance against selfish-mining and double-spend attacks. For a double-spend attacker with 40% mining power, it will succeed in Bitcoin with a probability ≥ 35%, in contrast, his probability is less than 1% in LVRT (for$k\geq 50$).

Group signature with verifier-local revocation (VLR-GS) is a special variant of revocable group signature that not only allows a user to anonymously sign messages but also only requires the verifiers to possess some up-to-date revocation information. To date, a number of VLR-GS schemes have been proposed under bilinear groups and lattices, while they have not yet been instantiated based on coding theory. In this paper, we present a code-based VLR-GS scheme in the random oracle model, which is the first construction to the best of our knowledge. Concretely, our VLR-GS scheme does not rely on the traditional paradigm which utilizes an encryption scheme as a building block and achieves logarithmic-size group signature. To obtain the scheme, we first introduce a new code-based Stern-like interactive zero-knowledge protocol with member revocation mechanism based on syndrome decoding problem. Moreover, we employ the binary Goppa code embedded for our scheme with efficiency and security analysis.

With the rise of third parties in the machine learning pipeline, the service provider in “Machine Learning as a Service” (MLaaS), or external data contributors in online learning, or the retraining of existing models, the need to ensure the security of the resulting machine learning models has become an increasingly important topic. The security community has demonstrated that without transparency of the data and the resulting model, there exist many potential security risks, with new risks constantly being discovered. In this paper, we focus on one of these security risks - poisoning attacks. Specifically, we analyze how attackers may interfere with the results of regression learning by poisoning the training datasets. To this end, we analyze and develop a new poisoning attack algorithm. Our attack, termed Nopt, in contrast with previous poisoning attack algorithms, can produce larger errors with the same proportion of poisoning data-points. Furthermore, we also significantly improve the state-of-the-art defense algorithm, termed TRIM, proposed by Jagielsk et al. (IEEE S&P 2018), by incorporating the concept of probability estimation of clean data-points into the algorithm. Our new defense algorithm, termed Proda, demonstrates an increased effectiveness in reducing errors arising from the poisoning dataset through optimizing ensemble models. We highlight that the time complexity of TRIM had not been estimated; however, we deduce from their work that TRIM can take exponential time complexity in the worst-case scenario, in excess of Proda's logarithmic time. The performance of both our proposed attack and defense algorithms is extensively evaluated on four real-world datasets of housing prices, loans, health care, and bike sharing services. We hope that our work will inspire future research to develop more robust learning algorithms immune to poisoning attacks.

Self-orthogonal codes are an important type of linear codes due to their wide applications in communication and cryptography. The Euclidean (or Hermitian) hull of a linear code is defined to be the intersection of the code and its Euclidean (or Hermitian) dual. It is clear that the hull is self-orthogonal. The main goal of this paper is to obtain self-orthogonal codes by investigating the hulls. Let$\mathcal {C}_{(r,r^{m}-1,\delta,b)}$be the primitive BCH code over$\mathbb {F}_{r}$of length$r^{m}-1$with designed distance$\delta $, where$\mathbb {F}_{r}$is the finite field of order$r$. In this paper, we will present Euclidean (or Hermitian) self-orthogonal codes and determine their parameters by investigating the Euclidean (or Hermitian) hulls of some primitive BCH codes. Several sufficient and necessary conditions for primitive BCH codes with large Hermitian hulls are developed by presenting lower and upper bounds on their designed distances. Furthermore, some Hermitian self-orthogonal codes are proposed via the hulls of BCH codes and their parameters are also investigated. In addition, we determine the dimensions of the code$\mathcal {C}_{(r,r^{2}-1,\delta,1)}$and its hull in both Hermitian and Euclidean cases for$2 \le \delta \le r^{2}-1$. We also present two sufficient and necessary conditions on designed distances such that the hull has the largest dimension.

In this paper, a multi-user system adopting finite blocklength (FBL) channel codes for downlink (DL) transmissions under quality-of-service (QoS) constraints is considered. A framework for minimizing the total bandwidth to ensure ultrareliable and low-latency communications (URLLC) between a transmitter and multiple users is established. Frequency-selective fading channels are considered. It is assumed that channel state information (CSI) is perfectly known at the receiver. Effective capacity framework is employed to characterize the throughput under delay constraints with FBL channel codes. The relationship between the throughput and the bandwidth is studied. The optimization problem to minimize the total bandwidth under different QoS and minimum throughput limits is formulated. Furthermore, an efficient algorithm to obtain the optimal bandwidth allocation scheme is proposed. Overall, the impact of delay exponents, decoding error probability and minimum throughput on URLLC is characterized.

This paper proposes a new generative model called neural belief reasoner (NBR). It differs from previous models in that it specifies a belief function rather than a probability distribution. Its implementation consists of neural networks, fuzzy-set operations and belief-function operations, and query-answering, sample-generation and training algorithms are presented. This paper studies NBR in two tasks. The first is a synthetic unsupervised-learning task, which demonstrates NBR's ability to perform multi-hop reasoning, reasoning with uncertainty and reasoning about conflicting information. The second is supervised learning: a robust MNIST classifier for 4 and 9, which is the most challenging pair of digits. This classifier needs no adversarial training, and it substantially exceeds the state of the art in adversarial robustness as measured by the L2 metric, while at the same time maintains 99.1% accuracy on natural images.

Modern vehicles are generally equipped with dozens of (or even hundreds of) electronic and intelligent devices and bloom into more involved information hub in enabling V2X networking. Protecting this increasingly complex vehicle ecosystem can be an arduous task, especially as the proliferation of data across distinct connected devices makes them more vulnerable than ever before. Intrusion detection systems (IDSs) have been found extremely rewarding in monitoring in-vehicle network traffic and detecting potential intrusions. The paper presents WLOF-InV, a novel unsupervised method based on local density for IDS on in-vehicle network. Given historical in-vehicle data of message identifiers, WLOF-InV first segments the traffic into a slice of (e.g., m) sliding windows. For each sliding window, WLOF-InV exerts information gain to select features for dimensionality reduction and squeezes out n features which are then bundled together to form a row vector and eventually gets an m*n matrix. WLOF-InV then adaptively determines the hyper parameters for local outlier factor (LOF) model (optimizing the scores for ranking the training data and the cutoff position for anomalies). In online detection, WLOF-InV determines the features by the information gain and invokes abnormal score weighting mode (which weights the LOF value of each dimension data by entropy method) to obtain the complete LOF score (of the overall traffic), and thereby grabs the anomaly traffic by resorting to the adjusted model. WLOF-InV is validated on the real data of three attack types (DoS, fuzzy, and impersonation). Experimental results demonstrate that WLOF-InV contrives next to optimal performance.

To assure high quality of software systems, the comprehensiveness of the created test suite and efficiency of the adopted testing process are highly crucial, especially in the FinTech industry, due to a FinTech system’s complicated system logic, mission-critical nature, and large test suite. However, the state of the testing practice in the FinTech industry still heavily relies on manual efforts. Our recent research efforts contributed our previous approach as the first attempt to automate the testing process in China Foreign Exchange Trade System (CFETS) Information Technology Co. Ltd., a subsidiary of China’s Central Bank that provides China’s foreign exchange transactions, and revealed that automating test generation for such complex trading platform could help alleviate some of these manual efforts. In this paper, we investigate further the dilemmas faced in testing the CFETS trading platform, identify the importance of domain knowledge in its testing process, and propose a new approach of domain-specific test generation to further improve the effectiveness and efficiency of our previous approach in industrial settings. We also present findings of our empirical studies of conducting domain-specific testing on subsystems of the CFETS Trading Platform.

Cloud computing is a new promising technology paradigm that can provide clients from the whole network with scalable storage resources and on-demand high-quality services. However, security concerns are raised when sensitive data are outsourced. Searchable encryption is a kind of cryptographic primitive that enables clients to selectively retrieve encrypted data, the existing schemes that support for sub-linear boolean queries are only considered in symmetric key setting, which makes a limitation for being widely deployed in many cloud applications. In order to address this issue, we propose a novel searchable asymmetric encryption scheme to support for sub-linear boolean query over encrypted data in a multi-client model that is extracted from an important observation that the outsourced database in cloud is continuously contributed and searched by multiple clients. For the purpose of introducing the scheme, we combine both the ideas of symmetric searchable encryption and public key searchable encryption and then design a novel secure inverted index. Furthermore, a detailed security analysis for our scheme is given under the simulation-based security definition. Finally, we conduct experiments for our construction on a real dataset (Enron) along with a performance analysis to show its practicality.

This paper investigates the link selection policy for secure communications over a buffer-aided two-hop communication link. It is assumed that a source wishes to send information to a destination with the aid of a trusted half-duplex relay node compromised by an eavesdropper, and that there is no direct link between the source and the destination. The buffer-aided relay forwards the information to the destination by employing the randomize-and-forward scheme. Both the source and the relay transmissions are assumed to be vulnerable to the eavesdropper. The perfect channel side information of the network is assumed to be available at the source and the relay. Initially, conventional relaying protocols where equal partition of the time for the reception and transmission of the relay is considered. Then, the optimal time fraction with the optimal power control policy that maximizes the secrecy throughput is identified. Moreover, the optimal joint link selection and power control policy that maximizes the secrecy throughput is derived. Subsequently, a low complexity and asymptotically optimal link selection with ON/OFF power control is proposed. Numerical results demonstrate that the proposed link selection policies with power control can significantly improve the achievable secrecy throughput of the buffer-aided two-hop communication system.

Power supply fluctuation can be potential threat to the correct operations of processors, in the form of voltage emergency that happens when supply voltage drops below a certain threshold. Noise sensors (with either analog or digital outputs) can be placed in the nonfunction area of processors to detect voltage emergencies by monitoring the runtime voltage fluctuations. Our work addresses two important problems related to building a sensor-based voltage emergency detection system: 1) offline sensor placement, i.e., where to place the noise sensors so that the number and locations of sensors are optimized in order to strike a balance between design cost and chip reliability and 2) online voltage emergency detection, i.e., how to use these placed sensors to detect voltage emergencies in the hotspot locations. In this paper, we propose integrated solutions to these two problems, respectively, for analog and digital (more specifically, binary) sensor outputs, by exploiting the voltage correlation between the sensor candidate locations and the hotspot locations. For the analog case, we use the Group Lasso and an ordinary least squares approach; for the binary case, we integrate the Group Lasso and the SVM approach. Experimental results show that, our approach can achieve 2.3X-2.7X better voltage emergency detection results on average for analog outputs when compared to the state-of-the-art work; and for the binary case, on average our methodology can achieve up to 21% improvement in prediction accuracy compared to an approach called max-probability-no-prediction.

Outsourcing computation with verifiability is a merging notion in cloud computing, which enables lightweight clients to outsource costly computation tasks to the cloud and efficiently check the correctness of the result in the end. This advanced notion is more important in marine mobile computing since the oceangoing vessels are usually constrained with less storage and computation resources. In such a scenario, vessels always firstly outsource data set and perform a function computing over them or at first outsource computing functions and input data set into them. However, vessels may choose which delegation computation type to outsource, which generally depends on the actual circumstances. Hence, we propose a scalable verifiable outsourcing computation protocol ( SV-OC ) in marine cloud computing at first and extract a single-mode version of it ( SM-SV-OC ), where both protocols allow anyone who holds verification tokens to efficiently verify the computed result returned from cloud. In this way, the introduced “scalable” property lets vessels adjust the protocol to cope with different delegation situations in practice. We additionally prove both SV-OC and SM-SV-OC achieving selective soundness in the random oracle model and evaluate their performance in the end.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the $d$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using $\textsf{Python}$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.

Abstract Location‐based social discovery (LBSD) services enable users to discover their geographic neighborhoods to make new friends. Original LBSD services were designed to provide the exact distances to nearby users. It has been shown that it is easy to pinpoint any target user's location by using trilateration based on the exact distances from three fake Global Positioning System locations to the target user. To defend against the trilateration attack, contemporary LBSD services then began to report distances of nearby users in concentric bands, for example, bands of 100 meters, rather than exact distances. In this paper, we investigate the user location privacy leakage problem in LBSD services reporting distances in discrete bands. Using number theory, we analytically show that by strategically placing multiple virtual probes with fake Global Positioning System locations, one can nevertheless localize user locations in band‐based LBSD. Our methodology is guaranteed to localize any reported user within a circle of radius no greater than one meter, even for LBSD services using large bands (such as 100 m as used by WeChat). Eventually, countermeasures are proposed to reduce location privacy leakage to the very minimum. To the best of our knowledge, this is the first work that explicitly exploits and quantifies user location privacy leakage in band‐based LBSD services. We expect our study to draw more public attention to this serious privacy issue and expectantly motivate better privacy preserving LBSD designs. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Dual receiver encryption (DRE), a notion of public key encryption (PKE) introduced at CCS'04, allows two independent receivers to decrypt a ciphertext into a same plaintext. This crypto primitive is quite useful in designing denial of service attack‐resilient protocols. To our knowledge, prior DRE constructions are considered in the traditional PKE settings, which may face the difficulty of certificate management. This paper aiming at solving this dilemma of DRE in the traditional PKE settings, and gives an identity‐based variant version of DRE: identity‐based dual receiver encryption (ID‐DRE) that combines the notion of DRE and identity‐based encryption (IBE). Based on Waters' IBE (Crypto'05), two ID‐DRE schemes are constructed in the standard model and by partitioning methodology, provable security of our ID‐DRE schemes are obtained under the decisional bilinear Diffie‐Hellman. Furthermore, we achieve a tighter reduction by adopting a random walk ‐like methodology of analysis on the lower bound of simulators' artificial abort, which also results in better security tightness for Waters IBE. This improved result for Waters' IBE, where n is the bitlength of messages and q is the number of adversarial key queries, is consistent with Hofheinz and Kiltz's result (Crypto'08). Copyright © 2017 John Wiley & Sons, Ltd.

Cloud storage provides tremendous storage resources for both individual and enterprise users. In a cloud storage system, the data owned by a user are no longer possessed locally. Hence, it is not competent to ensure the integrity of the outsourced data using traditional data integrity checking methods. A privacy-preserving public auditing protocol allows a third party auditor to check the integrity of the outsourced data on behalf of the users without violating the privacy of the data. However, existing privacy-preserving public auditing protocols assume that the end devices of users are powerful enough to compute all costly operations in real time when the data to be outsourced are given. In fact, the end devices may also be those with low computation capabilities. In this paper, we propose two lightweight privacy-preserving public auditing protocols. Our protocols are based on online/offline signatures, by which an end device only needs to perform lightweight computations when a file to be outsourced is available. Besides, our proposals support batch auditing and data dynamics. Experiments show that our protocols are hundreds of times more efficient than a recent proposal regarding to the computational overhead on user side.

Noise margin violation, also known as voltage emergency induced by continuously reducing noise margin and increasing magnitude of current swings, is becoming a severe threat to the correct execution of applications in processors. Noise sensors can be placed in the non-function area of processors to detect such emergencies by monitoring runtime voltage fluctuations. In this work, we aim to accurately predict the voltage droops using a small set of sensors. We achieve our goal in two steps: We first propose a methodology via group lasso approach to select the optimal set of noise sensors, then build a practical model via ordinary least-squares fitting approach to predict the voltage in the function area of the chip, using the selected sensors in non-function area. Experiment results show that when compared to the full-chip voltage transient simulation, the prediction error of our model is much less than 0.01, and compared to prior work, our approach can achieve better error rates of voltage emergency detection (less than half).

We revisit the problem of basing signcryption SC tag key encapsulation mechanism KEM on standard assumptions and standard model and present direct constructions of SC-KEM/tag-KEM, which satisfy confidentiality and unforgeability with respect to adversarially chosen keys where the adversary is given more advantageous attack environment than existing models in the literature;are based on the standard decisional bilinear Diffie-Hellman and computational Diffie-Hellman assumptions without random oracle;do not use strongly unforgeable signature schemes as building blocks; andprovide comparable performance to existing SC-KEM/tag-KEM schemes.

In this work, a hybrid random walk method is proposed for the thermal analysis of integrated circuits. Preserving the advantage of generic random walk method (GRW), i.e. the suitability for simulating local hot-spots, the proposed techniques largely reduce its runtime for accurate high-resolution simulation, and is suitable for the realistic pyramid-shape IC model. This is achieved by combining the GRW and the floating random walk techniques, and a novel usage of rectangular cuboid transition domain. The techniques to handle the Neumann boundary and convective boundary in thermal simulation are also discussed. Numerical experiments on several IC test cases validate the efficiency and accuracy of the proposed techniques, and demonstrate more than 100X speedup over the GRW method.

A typical but trivial layout of alphanumeric buttons in the touchscreen setting is to arrange the 10 digits and 26 letters in a natural order. This arrangement does not take into account the frequencies of letters and digits when the users touch the buttons to key in their passwords or messages. We examine large scale datasets of over 141 million passwords collected from several leading websites for social networking, Internet forums, gaming, dating, and various other online service providers in China, and find that the distribution of letters in passwords is quite close to that in Chinese language. Based on the letter/digit frequencies, we further propose an alphanumeric button layout scheme with the following advantages: the buttons are clicked as uniformly as possible, so that the lifetime of the touchscreen can be prolonged and finger oil residues may scatter more evenly over the button area of the screen; and in the meantime, the movements of users' fingers are improved to enhance good user experience when inputting messages. The idea behind the layout is potentially applicable to diversified races.

Graphical password methods rely on human experience and hand selection (not well-quantified metric) to evaluate the appropriateness and the confusion of the challenge images. In this paper we propose to use for authentication Chinese characters, for which the entropy can be up to 9.65 (much larger than other languages). We first show an algorithmic framework to authenticate a user and then present an empirical analysis conducted at a university. The advantages of the framework include the following: the storage overhead is low; no personal experience or hand selection is involved; there is no predefined dictionary of likely choices; and it can be easily referenced by personal-style cues. Our study shows that the number of participants that prefer our framework is much close to that in favor of graphical passwords, with an interesting outcome that the two groups of participants present significantly distinct backgrounds. Our framework and graphical passwords can be used as candidate authentication methods for users with different backgrounds. We also measure user choices of patterns and find that there is a slight preference of the 3$\times$3 grid to the circle patterns. While the proposed framework prescribes the challenge characters, the users have the option to define challenge characters of their own.

Power consumption has become a major consideration in nanometer chip design. Since the dynamic power is proportional to V dd2, and the static power is proportional to V dd, lowering power supply voltage is an efficient method to reduce the power usage.

The design complexity of modern high performance processors calls for innovative design techniques and methodologies for achieving time-to-market goals. New design techniques are also needed to curtail power increases that inherently arise from ever increasing performance targets. This paper describes new processor design and optimization approaches that bridge the gap between high performance and low power. These techniques are flexible as they rely on automated synthesis-centric optimizations to enable power reduction without sacrificing performance. These methodology innovations contributed to the industry leading performance of the POWER8 processor.

ABSTRACT In password‐based or two‐factor (password and smart card) authentications, password changing is one of common techniques used to improve the security of the systems protected by the password. However, the password‐changing operations in existing password authentications either depend on the login phase or violate the common practice that an old password should not be valid for subsequent login after being updated. On the other hand, password mistyping is very common in reality, which may be random or be skewed by the adversary via technical means or social engineering manipulation [i.e., a kind of denial‐of‐service (DoS) attack]. In human‐centric authentication mechanisms, password changing and DoS resilience are not marginal issues. The paper addresses the requirements of robust password changing in authentication and presents , a password authentication scheme with robust password changing, DoS resilience, and card‐compromise security. Thus, the proposal can be viewed as a suitable candidate instantiation for authentication services of human‐centric security, by embedding in the computer and software systems. also achieves other appealing features, such as self‐healing ability and strong privacy protection, which may be useful for human‐centric applications. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Optimal signatures (generating signatures as short as possible), which achieve the optimal bandwidth for communication, are extremely useful in bandwidth‐critical networks. Previous approaches use the random permutations with large block size as building blocks, which incurs less efficient implementations in the real world. Meanwhile, all the practical signature schemes are not optimal in bandwidth including PSS‐R (probabilistic signature scheme with message recovery ), FDH ( Full Domain Hash), and DSA (Digital Signature Algorithm). This paper presents three constructions for optimal signature schemes. All the proposals use both the random oracles and the ideal ciphers with smaller block sizes as building blocks to obtain optimal paddings for signature schemes. The ideal ciphers in our schemes can be implemented by real block ciphers (e.g., AES (Advanced Encryption Standard)‐256). Concrete implementations of these signature schemes can utilize the trapdoor permutations of Rabin and RSA, respectively. Surprisingly, RSA and Rabin (trapdoor permutations) lead to not only optimality in bandwidth but also a tight security. Therefore, besides yielding secure signatures with high efficiency, our proposals can also be flexibly applied to the bandwidth‐limited networks that reduces the communication cost as less as possible. Copyright © 2014 John Wiley & Sons, Ltd.

For accurate thermal simulation of integrated circuits (ICs), heat sink components in chip package must be considered. In this letter, techniques based on the domain decomposition method (DDM) are presented for the 3-D thermal simulation of nonrectangular IC thermal model including heat sink and heat spreader. A relaxed nonoverlapping DDM algorithm is employed to convert the problem to subproblems on rectangular subdomains. Then, a nonconformal discretization strategy is proposed to reduce the problem complexity with negligible error. Numerical experiments on several 2-D and 3-D IC test cases demonstrate that the relaxed nonoverlapping DDM is faster than the other preconditioned conjugate gradient algorithms with same mesh grid. The nonconformal discretization achieves further 10× reduction of runtime and memory usage.

Full-chip thermal monitoring is an important and challenging issue in today's microprocessor design. In this paper, we propose a new information-theoretic framework to quantitatively model the uncertainty of on-chip temperature variation by differential entropy. Based on this framework, an efficient optimization scheme is developed to find the optimal spatial locations for temperature sensors such that the full-chip thermal map can be accurately captured with a minimum number of on-chip sensors. In addition, several efficient numerical algorithms are proposed to minimize the computational cost of the proposed entropy calculation and optimization. As will be demonstrated by our experimental examples, the proposed entropy-based method achieves superior accuracy (1.4x error reduction) for full-chip thermal monitoring over prior art.

A (worst-case) 2-source extractor is a deterministic algorithm that transforms pairwise independent weak random sources into almost uniform random strings. Despite non-constructive proofs that such objects exist with almost optimal parameters, it has been a longstanding open problem to construct `explicit' (aka efficient) functions for sources of `small' constant entropy rate. In particular, best known constructions either require entropy rate of at least 0.4999 (due to Bourgain), or one source must remain with constant entropy rate above half (due to Raz). Motivated by cryptographic applications, we observe that if one source is a leaky source (or it contains a few deterministically extractable entropy), then we will be able to efficiently extract almost all entropy from both sources with nearly optimal entropy loss. Further, our extractor (for leaky sources) does not suffer from the half entropy rate barrier, and it works for all linear (and even sub-linear) entropy sources. The extractor is constructed using the technique of alternating extraction by Dziembowski and Pietrzak (FOCS 2007). Finally, we show that the extractor is almost a worse-case extractor (for the same parameters) in the sense that it only fails for a negligible fraction of sources.

ABSTRACT Anonymous password authenticated key exchange (APAKE) protocols allow the server to authenticate its clients without revealing their identities. In this paper, we first construct a basic protocol SAPAKE by using the homomorphic encryption scheme and an auxiliary memory device. Compared with the previous ones, SAPAKE is more suitable for those privacy‐sensitive applications (e.g., cloud computing) where reducing server payload and improving user experience are both essential. Furthermore, we refine SAPAKE by removing the use of the memory device to gain an enhanced extension SAPAKE+ without increasing the resources consumption. SAPAKE+ achieves better user‐friendliness than SAPAKE while it requires publishing more public parameters. Both of our protocols are practical due to their low (computation and communication) resources consumption and better user‐friendliness, and achieve provable security in the random oracle model. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT We consider public key encryption (PKE) schemes with auxiliary input, that is, the adversary is given any computationally uninvertible function of the secret key. Previous result only achieves security under chosen‐plaintext attacks (CPA). In this paper, we construct public key encryption schemes that are secure under chosen‐ciphertext attacks even when the adversary is given any computationally uninvertible function of the secret key as an auxiliary input. We follow the Naor–Yung ‘double encryption’ paradigm and generally transform any chosen‐plaintext attack secure public key encryption into a chosen‐ciphertext attack secure one in the auxiliary input model. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Blind signatures are generated by means of a protocol between the signer and a user such that the signer can neither see the message being signed and nor learn any information on the signature being produced. Time/space complexity and security model (random oracle model versus standard model; sequential, parallel, or concurrent security) are commonly used to evaluate blind signature schemes. The paper presents the first round‐optimal blind signatures without random oracles or non‐interactive zero‐knowledge proofs. The proposed blind signature scheme achieves concurrent security and perfect blindness while preserving the efficiency of computation and communication. A novel class of computational problems, called one‐more‐output (OMO) problems, is introduced to prove the unforgeability of the scheme. The paper states the corresponding lower bound of the OMO problem in the generic group model. Such a computational problem might be of independent interests in designing other cryptographic protocol and primitives. Copyright © 2011 John Wiley & Sons, Ltd.

A tree-driven clock grid has become the choice of clock delivery for most microprocessors, due to its ability to achieve lower skew and lower variability than clock trees, and is becoming the choice of clock delivery for certain high-end application-specific integrated circuit designs. This paper reports on a clock routing tool that was used in designing multiple tree-driven clock grids in a 2.3 GHz processor system-on-chip, which achieved below 5 ps skew within 500$\mu{\rm m}$Manhattan distance and below 10 ps skew across each clock grid. This clock routing tool employs a nonsequential algorithm comprised of linear programming and combinatorial heuristics. Its robust length-matching capability enables flexible buffer placement, improved clock signal quality, and robustness to variations.

Accurate and efficient thermal analysis for a VLSI chip is crucial, both for sign-off reliability verification and for design-time circuit optimization. To determine an accurate temperature profile, it is important to simulate a die together with its thermal mounts: this requires solving Poisson's equation on a nonrectangular 3D domain. This article presents a class of eigendecomposition-based Fast Poisson Solvers (FPS) for chip-level thermal analysis. We start with a solver that solves a rectangular 3D domain with mixed boundary conditions in O( N ⋅ log N ) time, where N is the dimension of the finite difference matrix. Then we reveal, for the first time in the literature, a strong relation between fast Poisson solvers and Green-function-based methods. Finally, we propose an FPS method that leverages the preconditioned conjugate gradient method to solve nonrectangular 3D domains efficiently. We demonstrate this approach on thermal analysis of an industrial microprocessor, showing accurate results verified by a commercial tool, and that it solves a system of dimension 4.54e6 in only 13 conjugate gradient iterations, with a runtime of 65 seconds, a 15X speedup over the popular ICCG solver.

In order to make people truly benefit from data sharing, we need technical solutions to assuring the trustworthiness of data received from parties one may not have encountered in the past. Assured data provenance is an important means for this purpose because it (i) allows data providers to get credited for their contribution or sharing of data, (ii) is able to hold the data providers accountable for the data they contributed, and (iii) enables the data providers to supply high-quality data in a self-healing fashion. While the above (i) and (ii) have been investigated to some extent, the above (iii) is a new perspective that, to our knowledge, has not been investigated in the literature. In this paper, we introduce a novel cryptographic technique that can simultaneously offer these properties. Our technique is called editable signatures, which allow a user, Bob, to edit (e.g., replace, modify, and insert) some portions of the message that is contributed and signed by Alice such that the resulting edited message is jointly signed by Alice and Bob in some fashion. While it is easy to see that the above (i) and (ii) are achieved, the above (iii) is also achieved because Bob may have a better knowledge of the situation that allows him to provide more accurate/trustworthy information than Alice, who may intentionally or unintentionally enter inaccurate or even misleading data into an information network. This is useful because Alice's inaccurate or even misleading information will never be released into an information network if it can be ``cleaned" or "healed" by Bob. Specifically, we propose two novel cryptographic constructions that can be used to realize the above functions in some practical settings.

This paper compares and contrasts two common clock distribution styles: clock grids, the preferred microprocessor distribution style, and clock trees, the preferred ASICs distribution style. After a high level description of the routing methodologies for clock grids and clock trees, a case study is presented to compare the performance and cost trade-off of grids and trees. Our results show that clock grids consume more power and wiring resources but only to achieve aggressive clock targets. In this example a clock tree style uses 28% less wiring than a full clock grid style but suffers 12 ps more skew. However, compared to a sparse grid style, a clock tree solution uses only 4% less wiring and suffers 9.6 ps higher skew. The key message is that the cost in extra wiring and power consumption across different clock distribution styles is mainly driven by performance targets as opposed to being fundamentally dictated by the grid vs. tree decision.

GOES Imager visible channel has no onboard calibration devices. While many methods of vicarious calibration exist, one of few viable options to render the calibration absolute is to make it traceable to MODIS. However, the spectral response function for the visible band of MODIS is very different from that of GOES Imager visible channel. In this study we use the hyperspectral data collected by GOME-2 to characterize the difference due to SRF under various conditions. It was found that this difference tends to be stable for bright clouds, although there are variations among GOES. This results offer guidance for target selection in inter-calibration with MODIS.

Accurate and efficient thermal analysis for a VLSI chip is crucial, both for sign-off reliability verification and for design-time circuit optimization. To determine an accurate temperature profile, it is important to simulate a die together with its thermal mounts: this requires solving Poisson's equation on a non-rectangular 3D domain. This paper presents a class of eigendecomposition-based fast Poisson solvers (FPS) for chiplevel thermal analysis. We start with a solver that solves a rectangular 3D domain with mixed boundary conditions in O(NlogN) time, where N is the dimension of the finite-difference matrix. Then we reveal, for the first time in the literature, a strong relation between fast Poisson solvers and Green-function-based methods. Finally, we propose an FPS method that leverages the preconditioned conjugate gradient method to solve non-rectangular 3D domains efficiently. We demonstrate that this approach solves a system of dimension 5.33e6 in only 11 Conjugate Gradient iterations, with a runtime of 171 seconds, a 6X speedup over the popular ICCG solver.

A blind signature is a very important technology in e-commerce. This paper uses an ideal cipher with a smaller block size to design a secure two-move blind signature with an optimal padding. Our new scheme has the message recovery property with less bandwidth, which means the user can recover the message from the signature signed by the signer, but costs no other bandwidth to save power and battery life. This blind signature can be implemented with a truly real block cipher. Besides this, We also give the scheme for longer message in our paper which uses one random oracles and an ideal cipher. Security analysis for the scheme is also provided in this paper.

With ever-shrinking device geometries, process variations play an increased role in determining the delay of a digital circuit. Under such variations, a gate may lie on the critical path of a manufactured die with a certain probability, called the criticality probability. In this paper, we present a new technique to compute the statistical criticality information in a digital circuit under process variations by linearly traversing the edges in its timing graph and dividing it into ldquozones.rdquo We investigate the sources of error in using tightness probabilities for criticality computation with Clark's statistical maximum formulation. The errors are dealt with using a new clustering-based pruning algorithm which greatly reduces the size of circuit-level cutsets improving both accuracy and runtime over the current state of the art. On large benchmark circuits, our clustering algorithm gives about a 250times speedup compared with a pairwise pruning strategy with similar accuracy in results. Coupled with a localized sampling technique, errors are reduced to around 5% of Monte Carlo simulations with large speedups in runtime.

Abstract In electronic communication and wireless communication, message authentication should be necessary. However, traditional method message authentication code (MAC) employs a symmetric cryptographical technique and it needs to keep a shared private key between two parties. For convenience, people now begins to use public key techniques to provide message authentication. In wireless communication, we shall save more space for message itself because of the limited resources. Therefore, we believe that our proposed digital signature scheme will be more fitful for this kind of communication due to the following merits: (1) in addition to inheriting the merits of RSA signature such as high verification efficiency, the proposed scheme also shows its advantage over RSA by resisting low public key exponent attack; (2) comparing with 1024 bits RSA, our digital signature scheme can sign 2048‐bit long message once, and generate a signature with 1025 bits length which doubles the capacity of the 1024‐bit RSA signature; (3) the scheme is provably secure and its security is tightly related to the hardness of conic‐based (CB)‐RSA assumption. Copyright © 2008 John Wiley & Sons, Ltd.

We present a new linear time technique to compute criticality information in a timing graph by dividing it into “zones”. Errors in using tightness probabilities for criticality computation are dealt with using a new clustering based pruning algorithm which greatly reduces the size of circuitlevel cutsets. Our clustering algorithm gives a 150X speedup compared to a pairwise pruning strategy in addition to ordering edges in a cutset to reduce errors due to Clark’s MAX formulation. The clustering based pruning strategy coupled with a localized sampling technique reduces errors to within 5% of Monte Carlo simulations with large speedups in runtime.

Dramatic rises in the power consumption and integration density of contemporary systems-on-chip (SoCs) have led to the need for careful attention to chip-level thermal integrity. High temperatures or uneven temperature distributions may result not only in reliability issues, but also timing failures, due to the temperature-dependent nature of chip time-to-failure and delay, respectively. To resolve these issues, high-quality, accurate thermal modeling and analysis, and thermally oriented placement optimizations, are essential prior to tapeout. This paper first presents an overview of thermal modeling and simulation methods, such as finite-difference time domain, finite element, model reduction, random walk, and Green-function based algorithms, that are appropriate for use in placement algorithms. Next, two-dimensional and three-dimensional thermal-aware placement algorithms such as matrix-synthesis, simulated annealing, partition-driven, and force directed are presented. Finally, future trends and challenges are described

This paper presents a new hybrid linear equation solver for quadratic placement. The new solver is a combination of stochastic solver and iterative solver: it is proven in this paper that an approximate LDL factorization can be obtained from random walks, and used as a preconditioner for conjugate gradient solver. Testing on real-life placement benchmarks shows a speedup of up to 7.1 times over traditional Incomplete Cholesky preconditioned Conjugate Gradient (ICCG).

High-performance integrated circuits are now reaching the 100-plus watt regime, and power delivery and power grid signal integrity have become critical. Analyzing the performance of the power delivery system requires knowledge of the current drawn by the functional blocks that comprise a typical hierarchical design. However, current designs are of such complexity that it is difficult for a designer to determine what a realistic worst-case switching pattern for the various blocks would be in order to maximize noise at a specific location. This paper uses information about the power dissipation of a chip to derive an upper bound on the worst-case voltage drop at an early stage of design. An exact integer linear programming (ILP) method is first developed, followed by an effective heuristic to speed up the exact method. A circuit of 43 K nodes is analyzed within 70 s, and the worst-case scenarios found correlate well with the results from an ILP solver.

This paper presents a class of power grid analyzers based on a random-walk technique. A generic algorithm is first demonstrated for dc analysis, with linear runtime and the desirable property of localizing computation. Next, by combining this generic analyzer with a divide-and-conquer strategy, a single-level hierarchical method is built and extended to multilevel and "virtual-layer" hierarchy. Experimental results show that these algorithms not only achieve speedups over the generic random-walk method, but also are more robust in solving various types of industrial circuits. Finally, capacitors and inductors are incorporated into the framework, and it is shown that transient analysis can be carried out efficiently. For example, dc analysis of a 71 K-node power grid with C4 pads takes 4.16 s; a 348 K-node wire-bond dc power grid is solved in 93.64 s; transient analysis of a 642 K-node power grid takes 2.1 s per timestep.

This work presents a chip-level charged device model (CDM) electrostatic discharge (ESD) simulation method. The chip-level simulation is formulated as a DC analysis problem. A network reduction algorithm based on random walks is proposed for rapid analysis, and to support incremental design. A benchmark with a 2.3M-node V/sub DD/ net and 1000 I/O pads is checked in 13 minutes, and 10 re-simulations for incremental changes take a total of 9 minutes.

High performance integrated circuits are now reaching the 100-plus watt regime, and power delivery and power grid signal integrity have become critical. Analyzing the performance of the power delivery system requires knowledge of the the current drawn by the functional blocks that comprise a typical hierarchical design. However, current designs are of such complexity that it is difficult for a designer to determine what a realistic worst-case switching pattern for the various blocks would be in order to maximize noise at a specific location. This paper uses information about the power dissipation of a chip to derive an upper bound on the worst-case voltage drop at an early stage of design. An exact ILP method is first developed, followed by an effective heuristic to speed up the exact method. A circuit of 43K nodes is analyzed within 70 seconds, and the worst-case scenarios found correlate well with the results from an ILP solver.

This paper presents a power grid analyzer based on a random walk technique. A linear-time algorithm is first demonstrated for DC analysis, and is then extended to perform transient analysis. The method has the desirable property of localizing computation, so that it shows massive benefits over conventional methods when only a small part of the grid is to be analyzed (for example, when the effects of small changes to the grid are to be examined). Even for the full analysis of the grid, experimental results show that the method is faster than existing approaches and has an acceptable error margin. This method has been applied to test circuits of up to 2.3M nodes. For example, for a circuit with 70K nodes, the solution time for a single node was 0.42 sec and the complete solution was obtained in 17.6 sec.

We propose the enhanced Fibonacci cube (EFC) structure for parallel systems. It is defined based on the sequence Fn = 2Fn−2+2Fn−4. We show that the enhanced Fibonacci cube contains the Fibonacci cube (FC) as a subgraph and maintains virtually all the desirable properties of the Fibonacci cube. In addition, it is a Hamiltonian graph. We can embed complete binary trees into enhanced Fibonacci cubes with dilation one and with a relatively small expansion. We also propose a series of enhanced Fibonacci cubes EFC(k), where k is a series number. Each EFC(k) contains an FC of the same order as a subcube. Moreover, each EFC(k) in the series contains any other cube that precedes it as subcubes and the last one in the series is a hypercube of the corresponding order. This series of EFC(k)s provides us with more options for selecting cubes with various sizes. Because EFC is a subgraph of the hypercube, it may find applications in fault-tolerant computing for degraded hypercube computer systems. As an application of EFC, we show that the parallel prefix sum computation can be efficiently implemented on enhanced Fibonacci cubes.

The enhanced Fibonacci cube (EFC) is defined based on the sequence F/sub n/=2F/sub n-2/+2F/sub n-4/. It contains the Fibonacci cube as a subgraph and maintains virtually all the desirable properties of the Fibonacci cube, and it also possesses properties such as the Hamiltonian property that the Fibonacci cube does not have. In this paper, we study data routing, namely, unicast, multicast and broadcast in the enhanced Fibonacci cube. The time and traffic steps are used to measure the efficiency of the routing algorithms. The unicast algorithm, which uses a Hamming distance path for any two nodes in EFC, is time and traffic optimal. The broadcast algorithm which employs the enhanced Fibonacci tree is traffic optimal and near time optimal. Two heuristic multicast algorithms are proposed which are based on an enhanced Fibonacci tree and a Hamiltonian cycle, respectively.

Large and complex software systems contain a variety of entities (objects) and a complex control system (transformation function). The pure object-oriented design and structured design approaches concentrate on either objects or the transformation function separately. As such they may not be adequate in isolation, to deal with the design of complex systems. Therefore, it makes sense to study their combination. We propose a combined functional and object-oriented design approach (CFOOD) based on the extended object-oriented design method proposed by P. Jalote (1989, 1991). The CFOOD approach makes full use of the object-oriented design and structured design techniques combining the object view and the functional view to provide a more complete view of a system. We demonstrate the use of our approach by a design example of a hospital patient monitoring system.