Fully Homomorphic Encryption (FHE) is a powerful privacy-preserving technology enabling secure computation on encrypted data, but it suffers from substantial performance overheads. Running FHE efficiently typically requires developing dedicated FHE accelerators, which can be costly and inflexible. Instead of pursuing entirely new accelerators, this paper explores an alternative paradigm: augmenting generic computing devices with a modular FHE-specific hardware extension unit (HEU) to create an efficient FHE engine. To make this paradigm viable, we propose three key innovations: (1) recognizing that some FHE operators are data-intensive and involve a massive volume of ciphertexts, we design the HEU with a 3D stacked memory-based architecture to handle data-intensive operators.We also provide software-level support to facilitate deploying FHE tasks on this extension-based architecture. (2) To capitalize on the hardware parallelism, we propose an adaptive offloading algorithm that intelligently distributes FHE operators between the computing device and the HEU. (3) To optimize the data layout and minimize the inter-tile data communications in the novel 3D stack memory, we propose a dedicated ciphertext mapping mechanism. Experimental results demonstrate that our work achieves substantial acceleration in FHE tasks.

Abstract In the Industrial Internet of Things (IIoT), the stealthiness and extensiveness of botnet attacks pose major security challenges, leading to potential data breaches and system outages. While deep learning techniques effectively detect botnets, their increasing complexity often necessitates adding more features. This not only elevates computational costs, but also hinders the timely detection of botnets. Therefore, there is a need for more effective strategies to enhance the security of IIoT systems. We propose an efficient IIoT botnet detection method, AdvBiTrans, that utilizes Pearson Correlation Coefficients Multi-stage Clustering Feature Selection (PMSFCS) and BiLSTM-Transformer framework (BiTrans) with PGD adversarial training, aiming to enhance detection accuracy and reduce feature dependency. The N-BaIoT and CIC-DDoS2019 datasets are analyzed, using data sampling methods to ensure balanced data. Experimental results showed that on the N-BaIoT dataset, the detection accuracy of the most prevalent malware families Mirai and Gafgyt reaches 99.94%, surpassing prior work by 0.44%, with an F1-score of 99.91%. On the CIC-DDoS2019 dataset, the detection accuracy reaches 97.79% after applying data balancing techniques, representing an improvement of 1.89%, with an F1-score of 99.88%.

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 large-scale deployment of Internet of Things (IoT) devices continuously generates spatio-temporal sensing data associated with time and location, which promotes the demand for privacy-preserving range-aggregate queries over such data. However, existing range-aggregate queries for IoT data only support aggregate computations over a single dimension, and fail to perform verification over range query result and aggregate computation. In this paper, we present VeriMIRA, a privacy-preserving range-aggregate query system over spatio-temporal IoT data with multidimensional numerical attributes. Technically, for range queries over outsourced spatio-temporal IoT data, we introduce new primitives of spatio-temporal prefix encoding (STPE) and verifiable and searchable spatio-temporal EF-Tree (VSEF-Tree), and combine improved symmetric homomorphic encryption (iSHE).For aggregate computation over multidimensional attributes, we introduce a new primitive of multi-client functional encryption with verifiable aggregation for inner product (VA-MCFE). By employing VSEF-Tree and VA-MCFE, the results of both range query and aggregation computation can be efficiently verified. In addition, we present security analysis on the introduced new primitives and VeriMIRA. Finally, we conduct extensive experiments, and evaluate the performance of existing solutions and VeriMIRA over a real-world dataset, which demonstrates practical computation and communication overhead for spatio-temporal range-aggregate queries. With sacrificing only 20% overhead in the data outsourcing stage, VeriMIRA achieves 70% time savings over state-of-the-art range-aggregate query approaches at 20,000 aggregations, while providing range-aggregate verification property.

Efficient power management in smart grid relies on collecting fine-grained power consumption data from users. However, these data may reveal sensitive information about individuals' habits and lifestyles. Various multidimensional data aggregation schemes leveraging public key encryption (PKE) algorithms have been proposed to address this problem. Never theless, most of these schemes come with significant performance costs. In addition, if the secret key of a smart meter was leaked, the confidentiality of encrypted user power data could be at risk. In this article, we propose a lightweight, multidimensional, and privacy-preserving data aggregation scheme with fault-tolerance and key-leakage resilience for smart grid without relying on a trusted third party (TTP), named FKLM-PDA, in which a novel data packaging method that transforms users' multidimensional data into a one-dimensional format is designed, enabling data center parse aggregated results in each dimension, reducing computation and communication costs. For better efficiency, an effective encryption algorithm is proposed to replace the expensive additive homomorphic PKE, like the Paillier cryptosystem, which combines a random masking with secret-sharing based key separation, ensuring threshold key-leakage resilience under a bounded, non-colluding leakage model. Furthermore, not only does FKLM-PDA enhance the fault tolerance mechanism of data transmission from smart meters to a corresponding fog node, but also it supports dynamic user management for joining and exiting improving scalability. Security analysis confirms that FKLM-PDA is privacy-preserving and secure while guaranteeing key-leakage resilience, fault tolerance, authentication, and data integrity. Through performance evaluations, FKLM-PDA outper forms the existing schemes and is superior in computation and functional in communication.

Privacy-preserving social graph search allows the retrieval of relationships within social networks while not com promising individuals' private information. Although numerous solutions enable conjunctive queries for relationships on encrypted social networks, the multi-client model is neglected despite its crucial role in collaborative data sharing, and personalized recommendations. In this paper, we present AuthGraph, a privacy-preserving and conjunctive social graph search system with trusted hardware in the multi-client (i.e., multi-writer/multi reader) model. In AuthGraph, a data owner delegates update rights to writers for maintaining dynamic social relationships, while readers are allowed to perform edge-weighted conjunctive queries via writer-enforced access policy. Technically, AuthGraph builds a multi-writer/multi-reader model with access control delegation for writers via a set-constrained pseudo-random function, and uses attribute-based encryption to configure authorizations for readers. To give a provably secure conjunctive search system over a dynamic social graph database, AuthGraph revisits oblivious dynamic cross tag protocol via providing comprehensive forward privacy and Type-O backward privacy. Different from previous solutions, in AuthGraph, the semi-black-box deployment of trusted hardware effectively ensures system security while maintaining performance. Finally, we demonstrate the performance of AuthGraph through extensive experiments on real social network datasets on AliCloud, revealing that the writer enforced access policy has minimal impact on search time cost.

Dynamic searchable symmetric encryption (DSSE) allows a client to update and retrieve its encrypted database stored on a server. To enable the database contributed by multi-writers to be searched by multi-readers, the multi-writer/multi-reader model is explored for DSSE (M/M-DSSE). Recently, FP-HSE (USENIX Security'22) and FP-MSE (TDSC'24) employed key aggregation methodology to introduce writer-selection property for M/M-DSSE, where a reader uses a single aggregate key to search the database containing selective writers. However, they neglected the reader-selection property where a writer selects which readers can search its data, and only supported single-keyword search. In this paper, we present BiMM, a conjunctive M/M-DSSE scheme with bilateral selection that simultaneously supports writer-selection and reader-selection properties. Additionally, BiMM achieves forward privacy (FP) for both server and client, and Type-O backward privacy (BP). Technically, we introduce a new primitive called bilateral key-aggregate encryption (Bi-KAE) that refines KAE via distributed key generation, by which both writers and readers can specify their selections. Based on ODXT (NDSS'21) that provides conjunctive queries while achieving FP with server and BP, we introduce double-blinded values to match all update records for the search keywords and additionally use the 0/1-Encoding technique to consider FP with client. Besides presenting formal security analysis for BiMM, we also conduct extensive experiments over public datasets on a real cloud server environment. The experiment results demonstrate that BiMM achieves practical performance for desired properties. With #keyword = 50 and #s-term = 10 for conjunctive queries, BiMM runs 6.34× faster than Nomos (AsiaCCS'24) which does not consider either writer-selection or reader-selection.

With the slowdown of Moore’s Law, conventional monolithic chip architectures face challenges such as excessive die sizes and prohibitive manufacturing costs. Consequently, chiplets have emerged as a pivotal technology in the post-Moore era, attracting significant attention from both academia and industry. Multi-chiplet systems offer compelling advantages over monolithic ones, including enhanced integration density, reduced cost, and shortened time-to-market. However, realizing these benefits necessitates design flows capable of optimizing parameters across logical, physical, and circuit layers, which introduces substantial design complexity. Numerous design automation technologies have been proposed to address these challenges. This article provides a comprehensive overview of related advancements, categorizing chiplet design methodologies into two primary types: (i) top-down flows disintegrating existing hardware designs into chiplets and subsequently reintegrating them into multi-chiplet systems, and (ii) bottom-up flows combining existing chiplets into multi-chiplet systems based on user applications. This article begins by introducing foundational concepts, technical characteristics, and evaluation models relevant to multi-chiplet systems. We then systematically summarize the problem formulations, design spaces, and optimization techniques associated with top-down and bottom-up design flows. Finally, we discuss key challenges and potential future research directions in chiplet design automation, aimed at further harnessing the potential of chiplet-based integration.

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.

ABSTRACT Several companies leverage trained convolutional neural networks (CNNs) to offer predictive services to users. These companies capitalize on CNNs' superior performance in image processing tasks, such as autonomous driving or face recognition. To safeguard data privacy and model parameters, various algorithms have been proposed. Most of them are predominantly designed using secure multi‐party computation (MPC) or hardware‐assisted solutions. However, certain limitations persist. First, MPC‐based approaches (e.g., garbled circuits, homomorphic encryption) fail to meet rapid responsiveness requirements. Additionally, hardware‐assisted solutions impose extra burdens to realize secure inference tasks. The primary reasons for these shortcomings can be summarized as follows: (1) high computation and communication delays are introduced by heavy cryptographic operations during the online phase. (2) Additional overhead for sharing triples. In this article, we propose PCIR, a secure protocol for privacy‐preserving convolutional neural network inference (PCIR). PCIR aims to address the aforementioned issues based on a pre‐shared secret sharing mechanism. It can achieve rapid responses to user requirements and preserve privacy of data and model for the following reasons: (1) it circumvents computationally expensive operations, such as an operation for permuting plaintext slots, which runs 56 times slower than a homomorphic addition operation, and 34 times slower than a homomorphic multiplication operation. (2) Computational operations, such as homomorphic additions or multiplications, are conducted during the pre‐computation phase. It can significantly reduce the online computing costs. (3) PCIR conducts secure multiplication based on pre‐shared secret shares. It results in much lower communication and computation costs compared with the use of multiplicative triples. Finally, we evaluate PCIR with benchmark neural networks trained on the MNIST and CIFAR‐10 datasets. The results have shown that PCIR requires less time and less communication cost than previous methodologies.

Abstract Precise forecasting of photovoltaic (PV) power generation upholds flexibility and reliability within the power grid. Due to the data security dilemma of previous forecasting methods, federated learning (FL) has been widely studied for its ability to train models without sharing training data. However, the incorrect behavior from untrusted devices and servers in traditional FL frameworks can undermine the integrity of the global model, precipitating inaccurate power generation forecasting. Therefore, we propose a blockchain-enabled trusted Byzantine-robust FL framework, called TBFL, designed for decentralized and privacy-preserving PV power generation forecasting. Specifically, this framework features a trusted supervision mechanism, which can effectively eliminate malicious gradients to achieve a high-quality model. In addition, a multilevel differential privacy scheme is designed to strike a balance between privacy protection and model accuracy. Finally, a model clipping algorithm based on neuronal similarity is implemented to optimize both the duration and consumption associated with local device training. Comprehensive experimental outcomes demonstrate that the framework TBFL can successfully improve robustness, and achieve similar efficiency as FedAvg while maintaining a high forecasting accuracy.

Federated learning (FL) prevents direct exposure of raw data. However, it remains vulnerable to privacy and security threats such as inference and poisoning attacks. Traditional differential privacy (DP) methods utilize noise injection to mitigate these attacks, which inherently degrades the accuracy of the model. In this paper, we propose a robust FL framework with two alternative effective defense mechanisms to enhance privacy preservation for various scenarios. We first propose a dual-layer client-server collaborative differential privacy (CLDP). Clients utilize adaptive local differential privacy (LDP) for data privacy, while the server uses central differential privacy (CDP) on the global model to mitigate poisoning attacks. Second, we propose enhanced central differential privacy (ECDP), a layer-specific protection mechanism that strategically injects targeted noise into non-batch normalization layers to further preserve data privacy. To mitigate noise-induced model performance degradation, our solution combines personalized data transformation and gradient sparsification, effectively alleviating both non-IID data distribution skew and cumulative noise effects. Architecturally, we decentralize the federated learning system through edge node integration, thereby eradicating single points of failure. Experimental results demonstrate that our framework achieves a superior accuracy-privacy trade-off under strict privacy constraints, providing robust protection without compromising practical utility.

Vehicular Sensor Networks (VSNs) are integral to intelligent transportation systems, enabling real-time communication and collaborative sensing among vehicles. However, their open wireless communication environment presents significant challenges in terms of data security and privacy protection. Although various certificateless aggregate signcryption (CLASC) schemes have been proposed to address these issues, many still suffer from security vulnerabilities and performance inefficiencies. We conduct a detailed security analysis of a recently proposed CLASC scheme by Dai et al., highlighting its susceptibility to public key replacement attacks. To demonstrate this vulnerability, we construct a specific attack algorithm and, based on our findings, propose a security-enhanced CLASC scheme. The proposed solution integrates blockchain technology to improve system decentralization, enhances resistance to collusion attacks, and supports malicious identity revocation mechanism. We provide formal security proofs under standard cryptographic hardness assumptions and evaluate the performance of the scheme through both theoretical analysis and experimental validation. The results show that our approach significantly strengthens security while maintaining high efficiency, making it well-suited for secure and scalable communication in vehicular sensor network (VSN) environments.

Mobile cloud storage enables IoT devices to use on-demand resources and share data with different mobile devices, where these outsourced data on the cloud are encrypted due to data confidentiality concern. Although dynamic searchable symmetric encryption (DSSE) allows data owners to directly search and update its encrypted data, it rarely considers implementing authorized search toward different mobile devices. Existing authorized keyword search systems for mobile cloud storage suffer from the following limitations: 1) only achieves Type-III backward privacy; 2) no support for verification of search result; and 3) incurs high time overhead for data update and search. Therefore, we propose$\textsf {VE}{-}\textsf {FLY}{++}$, an efficient, verifiable, and authorized DSSE system with forward and enhanced backward privacy for mobile cloud storage. Technically,$\textsf {VE}{-}\textsf {FLY}{++}$presents a verifiable inverted bitmap index (VIBI) to achieve forward privacy and enhanced Type-I (a.k.a.,$\textrm {Type-I}^{-}$) backward privacy, with supporting verification of search results. In addition, we combine symmetric encryption with homomorphic addition with the introduced VIBI for a fast authorized search function. To further enable efficiently handling hundreds of millions of files, we adopt chunking technology to present a highly scalable$\textsf {VE}{-}\textsf {FLY}{++}$. Finally, we use Raspberry Pi, Rock Pi, and Huawei Cloud on real datasets to conduct extensive experiments to clarify the practical efficiency of$\textsf {VE}{-}\textsf {FLY}{++}$.

In the cloud-assisted Industrial Internet of Things (IIoT), flexible and secure data sharing promotes industry processes optimization and new products making. To enable selective data retrieval over categorized data collected by IoT devices in the encryption domain, key-aggregate searchable encryption (KASE) is adopted for group data sharing in IIoT due to efficient management of encryption keys. Nonetheless, existing solutions rarely consider user revocation property and suffered from the following limitations: 1) the user revocation needs interaction process between data owner and cloud and 2) the nonrevoked users may be accidentally revoked. Therefore, we propose a blockchain-based revocable KASE system (BC-RKASE) that achieves noninteractive user revocation and trusted revoked user management. Technically, the result is nontrivially obtained from revisiting the generation of aggregate keys and key updates via secret sharing, along with redesigning trapdoor adjustment and providing public verifiable proof for key updates by public blockchain. Besides formal security analysis, we conduct extensive experiments in a real cloud environment and thin IoT devices (Raspberry Pi) using a public IIoT dataset to confirm the practical performance of BC-RKASE. In particular, BC-RKASE outperforms state-of-the-art schemes with$13.4{\times {\sim }}15\times $time efficiency accelerating for data retrieval and runs more than$16\times $faster for user revocation.

While the collection of users’ live or periodic electricity consumption data brings significant advantages for the operation of smart grids, it also heightens the risk of user privacy leakage. Numerous data aggregation schemes have been proposed to address this issue. However, most of these schemes either fail to accommodate the need for multifunctional data analysis or rely on a trusted third party (TTP). Given the efficient data processing capabilities offered by fog computing, we propose a blockchain-based privacy-preserving data aggregation (BPRM) scheme supporting multifunctionality for fog-assisted smart grid without TTP. This scheme ensures data confidentiality and data integrity while providing various statistical functions. In addition, we implement a consensus mechanism between smart meters, further enhancing the security and robustness of the smart grid system. Moreover, not only does the proposed the batch verification reduce the authentication costs but also support error detection in signatures. With BPRM, data center can calculate multiple statistical functions, achieving a win-win strategy. Extensive security and performance analyses demonstrate that BPRM can withstand various security threats and effectively protect user privacy while maintaining efficiency in both computational and communication overhead.

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.

Many network middleboxes have been deployed to performdeep packet inspection(DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted.BlindBox(SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposalPrivDPI(CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposalP2DPI(ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and presentPrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique calleddual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common.

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$\%$.

As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “Key Forge Attack” and “Trapdoor Forge Attack” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA$^{2}$SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA$^{2}$SE and prove its security. Finally, empirical evaluations demonstrate that KA$^{2}$SE is computationally efficient in comparison with existing schemes.

Abstract With the increasing strain on today’s healthcare resources, there is a growing demand for pre-diagnosis testing. In response, researchers have suggested diverse machine learning models for disease prediction, among which logistic regression stands out as one of the most effective models. Its objective is to enhance the accuracy and efficiency of pre-diagnosis testing, thereby alleviating the burden on healthcare resources. However, when multiple medical institutions collaborate to train models, the untrusted cloud server may pose a risk of private data leakage, enabling participants to steal data from one another. Existing privacy-preserving methods often suffer from drawbacks such as high communication costs, long training times and lack of security proofs. Therefore, it is imperative to jointly train an excellent model collaboratively and uphold data privacy. In this paper, we develop a highly optimized two-party logistic regression algorithm based on CKKS scheme. The algorithm optimizes ciphertext operations by employing ciphertext segmentation and minimizing the multiplication depth, resulting in time savings. Furthermore, it utilizes least squares to approximate sigmoid functions within specific intervals that cannot be handled by homomorphic encryption. Finally, the proposed algorithm is evaluated on a breast cancer dataset, and simulation experiments demonstrate that the model’s prediction accuracy, after machine learning training, exceeds 96% for two-sided encrypted data.

Abstract With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes.

Abstract Security of computer information can be improved with the use of a network intrusion detection system. Since the network environment is becoming more complex, more and more new methods of attacking the network have emerged, making the original intrusion detection methods ineffective. Increased network activity also causes intrusion detection systems to identify errors more frequently. We suggest a new intrusion detection technique in this research that combines a Convolutional Neural Network (CNN) model with a Bi-directional Long Short-term Memory Network (BiLSTM) model for adding attention mechanisms. We distinguish our model from existing methods in three ways. First, we use the NCR-SMOTE algorithm to resample the dataset. Secondly, we use recursive feature elimination method based on extreme random tree to select features. Thirdly, we improve the profitability and accuracy of predictions by adding attention mechanism to CNN-BiLSTM. This experiment uses UNSW-UB15 dataset composed of real traffic, and the accuracy rate of multi-classification is 84.5$\%$; the accuracy rate of multi-classification in CSE-IC-IDS2018 dataset reached 98.3$\%$.

Abstract Searchable asymmetric encryption (SAE) enables a client to search over a data owner’s encrypted data. Nevertheless, state-of-the-art SAE schemes allow a data owner to specify access control policy for a client, while they have not considered the threat case of a malicious data owner. To address the problem, this work presents a non-interactive SAE scheme with bilateral access control: (i) allowing data owner and client to both specify policies toward the other party; (ii) allowing client to perform arbitrary boolean queries with sub-linear search complexity. Technically, we extend Cash et al.’s highly scalable SSE into an asymmetric setting and introduce the property of data owner authenticity. By refining identity-based matchmaking encryption, we formalize the syntax and security definition of our SAE with identity-based bilateral access control. Moreover, the security of the proposed SAE can be reduced to discrete logistic assumption and decisional bilinear Diffie–Hellman assumption. As an enhanced extension, we present a non-interactive multi-client SAE scheme with fuzzy identity-based bilateral access control. In addition, we implement the proposed schemes in real cloud platform and evaluate their performance on a real-world dataset. The result confirms that our SAE schemes achieve bilateral access control for both data owner and client with highly acceptable efficiency.

In the digital era, one of the most significant changes in the IoT world is the popularity of digital subscriptions, where service providers upload encrypted service information to the cloud for sharing. In practice, the untrustworthy service providers may intentionally leak their private keys used to encrypt service information (for profits), allowing unauthorized subscribers to enjoy valuable service. The malicious behavior described above has become a severe obstacle to the widespread application of IoT-based digital subscriptions. To address this issue, we propose a fine-grained and sanitizable access control system (FSAC), in which service information could only be accessed by authorized subscribers. To thwart potential threats from malicious service providers, we design a sanitizable mechanism to transform the original ciphertext, ensuring that a subscriber is unable to decrypt the sanitized ciphertext solely using the leaked key of the service provider. For resource-constrained IoT devices, we further extend FSAC with outsourced decryption (FSACO) that relieves subscribers from the burden of decryption. In particular, FSACO allows subscribers to perform two exponentiation operations rather than time-consuming paring operations (as that in FSAC) to decrypt sanitized ciphertext. We conduct rigorous security analysis of our systems and demonstrate their efficient performance through extensive experiments. Specifically, the enhanced system FSACO has a minimum decryption time of approximately 0.08 ms.

Vehicular ad-hoc networks (VANETs) can help facilitate traffic flow, reduce accidents, and enhance the driving experience. However, VANETs have some problems in terms of the authenticity and integrity of transmitted information and the preservation of vehicles’ privacy. Many certificateless aggregate signature (CLAS) schemes have been proposed to address these concerns. Nevertheless, most of these schemes suffer from security and efficiency challenges, such as the inability to resist forgery attacks and high computation costs. Recently, an efficient CLAS scheme with conditional privacy protection has been put forward by Chen et al. However, there is a security flaw in this scheme. In this paper, we give a specific attack algorithm to indicate that Chen et al.’s proposal cannot resist a public key replacement attack initiated by external adversaries and then put forward a security-enhanced scheme. Furthermore, an efficient invalid signature identification algorithm is designed to identify invalid signatures after an aggregate verification has failed. Through rigorous security analysis, it has been verified that the scheme put forward can satisfy the fundamental security requirements of VANETs. Compared with other related schemes, our proposal improves efficiency while providing privacy and security guarantees for VANETs.

Blockchain-based Industrial Internet of Things (IIoT) integrates the blockchain technology into the traditional IIoT infrastructure to provide secure and collaborative services. In IIoT, the traffic is usually encrypted using a cipher suite (SSL/TLS) for secure communication, which makes it hard for middleboxes (MBs) to detect malicious activity in the traffic. To address this problem, secure MBs that directly perform encrypted traffic inspection have been presented. Recently, a new privacypreserving deep packet inspection (DPI) system on MB for IoT scenarios was proposed, but it suffered from the following two limitations: 1) no support for fast token detection and 2) no support for tracing abnormal sources. To address the two limitations, we propose BTDPI, a privacy-preserving traceable DPI system that efficiently performs inspection over encrypted traffic in blockchain-based IIoT. Technically, we adopt a two-layer filter architecture to improve the efficiency of detection and moreover introduce a new online–offline certificateless aggregate signature with smart contract to design an identity traceability mechanism. The experiment result shows that BTDPI runs$26.7\times $faster for token detection with 3000 tokens and 3000 rules than the state-of-the-art work.

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.

Secure cloud storage offers encrypted databases outsourcing service for resource-constrained clients, containing numerous tables with certain relations. Searchable symmetric encryption enables a client to search over its encrypted database on the cloud, while rarely considering queries over joins of tables. Join Cross-Tags (JXT) protocol (ASIACRYPT 2022) is thence presented that enables conjunctive queries over joins of tables, while neglecting arbitrary Boolean queries with disjunctive and conjunctive normal forms (DNF/CNF) in TWINSSE (PETS 2023). However, trivially combining JXT and TWINSSE for arbitrary DNF/CNF boolean queries over joins of tables seems infeasible due to: (i) no support for dis/conjunctive query with the same meta-keyword; (ii) returning inaccurate search results; (iii) incurring costly storage overhead. Therefore, we introduce TNT-QJ, a practical TwiN cross-Tag protocol for arbitrary boolean Query-Join over multi-tables. The result is technically obtained from revisiting TWINSSE’s framework via using s-term (the least frequent keyword) for the relation between a keyword and its meta-keyword, and non-trivially combined with JXT’s query-join approach for introducing a connective attributed in encryption tuples. In addition, we present a semi-full multi-fork searchable tree to store keyword information and reveal keyword containment relations, where the storage consumption is reduced from$\mathcal {O}(n^{3})$to$\mathcal {O}(n^{2})$. Finally, to clarify practical performance, we conduct extensive experiments on JXT and TNT-QJ using an open database in the HUAWEI cloud. Besides enabling disjunctive queries over joins of tables, TNT-QJ also runs$1.2\times $faster for conjunctive queries than JXT (with #keywords=2), which confirms rich features and practical efficiency.

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.

Abstract Public key encryption with keyword search is a promising primitive which enables search over encrypted data in secure data outsourcing services. In traditional construction, the associated keywords may be recovered from a given trapdoor by a malicious server through keyword guessing attacks. Therefore, the notion of public-key authenticated encryption with keyword search (PAEKS) was introduced, where a sender encrypts (and authenticates) the keywords using a receiver’s public key and its secret key. In this paper, we consider the forward security for PAEKS and introduce a new primitive: forward secure public-key authenticated encryption with keyword search (FS-PAEKS), which captures the information leakage risk from previously issued queries due to the updates on the outsourced data. Technically, we embed a non-interactively agreed key into the cipher-keyword generation algorithm, and bind the cipher-keyword and the trapdoor with a set converted from algorithm-generation time. Finally, we present an efficient FS-PAEKS scheme supporting conjunctive query, and prove its forward security against chosen keyword attacks and keyword guessing attacks. To illustrate practical performance, we implement our FS-PAEKS and related PAEKS schemes based on Enron dataset in real cloud environment.

Abstract Dynamic searchable symmetric encryption (DSSE) with forward and backward privacy makes it possible to perform search on the outsourced encrypted database efficiently while still allowing updates under acceptable leakage. Current forward and backward private DSSE (FB-DSSE) scheme proposed by Zuo et al. cannot support conjunctive keyword query and the cloud server needs to be honest-but-curious. Recent FB-DSSE scheme supporting conjunctive keyword query proposed by Patranabis et al. cannot verify search results. On the other hand, searchable symmetric encryption scheme proposed by Wang et al. that supports conjunctive keyword query and the verification of search results cannot achieve forward and backward privacy. The problem of constructing a verifiable conjunctive FB-DSSE scheme is still open. In this paper, we propose a verifiable conjunctive dynamic searchable symmetric encryption scheme (VCDSSE). VCDSSE is a FB-DSSE scheme that additionally supports the verification of search results and conjunctive keyword query. We revisit homomorphic MAC to enable efficient verification of search results, adopt the technique of oblivious cross-tags to achieve conjunctive keyword query and utilize state chain to ensure forward and backward privacy. The formal security analysis and performance evaluation demonstrate that VCDSSE is secure and practical as compared with Mitra scheme in terms of search time.

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.

Secure outsourced middleboxes are deployed in network function virtualization services that detect malicious activities on communications, which provides privacy-preserving deep packet inspection (DPI) over encrypted traffic. To boost filtering efficiency of packets, the two-layer middlebox architecture has been adopted in recent DPI systems. Nevertheless, state-of-the-art solutions based on two-layer architecture mainly suffer from two limitations: i) cannot support dynamic rule addition; ii) failed to inspect discontinuous token for rule matching. To address these limitations, this work proposes an efficient, dynamic and continuous DPI (DCDPI) system in secure outsourced middleboxes. To achieve dynamic rule addition with forward privacy, we refine a data structure called virtual binary tree (VBTree) and further introduce a variant of VBTree for DCDPI, termed VBTree+. VBTree+ supports two new desirable features: i) taking the rule action information into consideration; ii) achieving both rule identifier and rule action hiding. By introducing a token continuity check mechanism, DCDPI can effectively identify discontinuous tokens and categorize continuous tokens into one group. The extensive experiment over the real dataset and rule set confirms the practicality and efficiency of DCDPI. Compared to state-of-the-art works with same setting, DCDPI is 18%$\sim$110% more efficient for a connection establishment between gateway/client and server.

Cloud-based publish-subscribe (pub-sub) services provide a decoupling method for publishers and subscribers to effectively exchange targeted information and massive data on the cloud platform. Data publishers implement fine-grained access control to set subscription privileges for outsourced data through an access policy. However, in the context of semi-honest cloud platforms, the publisher's access policy may be collected, and incomplete or incorrect subscription results may be returned (e.g., to save communication costs). Existing solutions pay little attention to protecting the data publisher's access policy and cannot provide efficient verification for local results. In this article, we propose a verifiable multi-keyword data publish-subscribe scheme with a hidden access policy (VMP/S). Specifically, VMP/S combines attribute-based keyword search and data aggregation technology to achieve secure fine-grained access control, thereby protecting the privacy of the access policy. Additionally, the scheme provides an effective method for verifying local results by using equal-length verification information to confirm the correctness of feedback subscription data. Furthermore, we introduce a novel verification method for access control to enhance subscription performance efficiency. We demonstrate that VMP/S achieves IND-CKA security and ensures the privacy of the access policy through a comprehensive security analysis. Through experimental simulations, we confirm its effectiveness.

Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs: (i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30$\sim 200\times$smaller public parameter and secret key size, with the assumed least frequent keyword in a query ($s$-term) as 21. Moreover, it runs 10$\sim 20\times$faster for file encryption and$>20\times$faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with$s$-term=1 (resp. =21), as compared to classic dynamic ABKS system.

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.

Secure cloud-assisted data publish/subscribe (Pub/Sub) service provides an asynchronous method for publishers and subscribers to non-interactively exchange encrypted messages. Besides performing conjunctive subscription policy, numerous data Pub/Sub systems have recently been proposed to provide dynamic access control enforced from the publisher side to the subscriber side. However, these solutions fail to consider the following properties: (i) bilateral access control for both publishers and subscribers; (ii) the anonymity of the publisher; (iii) high matching time cost between publication and subscription. Therefore, we present P/S-BiAC, a secure and boolean cloud-assisted data Pub/Sub system with attribute-based bilateral access control that achieves authenticity and anonymity of publishers. In particular, P/S-BiAC enables cloud-based brokers to use the subscriber’s trapdoor to match published data with sub-linear time complexity. Technically, we introduce a “BiAC-and-Hidden” technique to refine publication tuples and trapdoor in classic searchable symmetric encryption solutions. Moreover, we implement P/S-BiAC and evaluate its practical performance based on Enron dataset in real cloud environment. To deal with a conjunctive subscription policy, P/S-BiAC runs 27.8× faster for matching time cost (with s-term=10) compared to state-of-the-art solutions, which demonstrates its feasibility in practical data Pub/Sub services with strong security properties.

Searchable encryption (SE) is a promising strategy for cloud-based file retrieval services, via structuring correspondences between files and keywords. Public key encryption with keyword search (PEKS) has been generally employed in file-sharing services, as compared to searchable symmetric encryption (SSE). However, PEKS is inherently vulnerable to keyword guessing attacks (KGA) launched by a malicious server. To resist such attacks, classic solutions are dual-server PEKS (DS-PEKS) [TIFS’2015] and server-aided PEKS (SA-PEKS) [TIFS’2016]. However, the query model in these two solutions only support single keyword search pattern, which inevitably limits their wide deployments in practice due to efficiency concern. In this work, we present DSB-SE, a new cloud-based file sharing & retrieval system that supports boolean queries while retaining KGA-resistance. Compared to DS-PEKS and SA-PEKS, the cost of documents searching in DSB-SE is 25, 000 times (resp. 6, 600 times) faster when$\#\text {keyword}=10$and$s\text {-term}=1$, where$s$-term is the least frequent keyword in the query pattern. Technically, the performance gain derives from revisiting traditional boolean SSE by: (i) introducing a pairing-free DDH-based transformation key modular that allows a data reader’s query pattern to be treated as a data writer’s; (ii) employing the dual-server methodology to support boolean query with efficient validity checks. In particular, the client-to-cloud communication cost for retrieving index of a single document is bounded to$10^{-2}s$, and the cost of sending a token ranges from$8\times 10^{-2}s\sim 13\times 10^{-2}s$. Nevertheless, DSB-SE is$1.5\times 10^{-2}s$slightly slower than DS-PEKS (but$1.35\times 10^{-2}s$faster than SA-PEKS) for key generation cost. Overall, the experiments show that the DSB-SE is practical and sufficient for real cloud applications, which is conducted over Enron dataset under a real-world cloud platform.

The emergence of edge computing brings data processing and storage to the vicinity of terminal equipment, which can quickly respond to user needs and reduce the computational burden of the traditional centralized cloud computing model, resulting in a model of edge computing-assisted cloud computing. In this architecture, how to prevent other untrusted entities from leaking user privacy has become one of the most critical concerns. To address this concern, many cryptographic schemes supporting the traditional cloud model to protect the data security sharing of IoT devices have been proposed. However, resource-constrained devices are an essential component of the Internet of Things (IoT). Its characteristics are one of the main reasons that affect the efficiency of schemes, and traditional cryptographic schemes are not suitable for edge computing. Therefore, in order to ensure secure data sharing between IoT devices, we come up with an improved certificateless online/offline signcryption (CLOOSC) scheme and achieve lower computational overhead, when offline calculation is not considered, the resource-constrained IoT device under the cloud-edge collaboration architecture requires only one point multiplication, while only one bilinear pairing is required in the verification phase. In the random oracle model, our scheme is proved to be IND-CCA2 secure. The experiment results show our scheme can be lightweight in terms of time cost.

In smart grids, a major challenge is how to effectively utilize consumers’ energy consumption data while preserving security and privacy. In this article, we tackle this challenging issue and focus on energy theft detection, which is very important for smart grids. Specifically, we note that most existing energy theft detection schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. To address this issue, we propose a novel privacy-preserving federated learning framework for energy theft detection, namely, FedDetect. In our framework, we consider a federated learning system that consists of a data center (DC), a control center (CC), and multiple detection stations. In this system, each detection station (DTS) can only observe data from local consumers, which can use a local differential privacy (LDP) scheme to process their data to preserve privacy. To facilitate the training of the model, we design a secure protocol so that detection stations can send encrypted training parameters to the CC and the DC, which then use homomorphic encryption to calculate the aggregated parameters and return updated model parameters to detection stations. In our study, we prove the security of the proposed protocol with solid security analysis. To detect energy theft, we design a deep learning model based on the state-of-the-art temporal convolutional network (TCN). Finally, we conduct extensive data-driven experiments using a real-energy consumption data set. The experimental results demonstrate that the proposed federated learning framework can achieve high accuracy of detection with a smaller computation overhead.

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.

Secure cloud search service allows resource-constrained clients to effectively search over encrypted cloud storage. Towards enabling owner-enforced search authorization, the notion of attribute-based keyword search (ABKS) has been introduced and widely deployed in practice. To enhance traditional security of ABKS, two state-of-the-art solutions are presented to address keyword guessing attacks or setup inconsistency for secret key. Nevertheless, they have not simultaneously considered the following threats to a data user: (i) inconsistent secret key/cipher-index caused by outside dishonest authority and/or data owner; (ii) algorithm substitution attacks (ASA) launched by inside adversarial eavesdropping. These attacks may unfortunately lead to cloud data breach and user information exposure. To tackle such outside and inside threats, we introduce subversion-resistance and consistency for secure and fine-grained cloud document search services. In particular, we propose a consistent ABKS system with cryptographic reverse firewalls (CRF). Technically, we refer to verifiable functional encryption and employ non-interactive zero-knowledge proofs of discrete logarithm equality to ensure strong input consistency for ABKS. In addition, we build a trusted CRF zone for sanitizing algorithm outputs against ASA attacks. Moreover, we formalize the security model and formally prove security of our system. To clarify practical performance, we implement state-of-the-art solutions and our system in real cloud environment based on Enron dataset. The results show that our system achieves more enhanced security properties without obviously sacrificing performance. In particular, our system achieves comparable time and storage cost for document-index encryption and document search, as compared to state-of-the-art solutions.

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.

To date, cloud computing has emerged as a primary utility for providing remote data storage services for users, since users can thus be relieved from cumbersome document maintenance. Despite of the benefits brought by data outsourcing, the unexpected data breaches raise concerns about data confidentiality and privacy. To deal with this, a straightforward and convincing strategy is to encrypt data before outsourcing them to the cloud. However, securely sharing and searching over outsourced encrypted data has turned into a challenge due to the hindrance led by data encryption. To address the challenge, this article proposes a new highly-scalable searchable encryption scheme for encrypted cloud storage. The scheme achieves sub-linear Boolean keyword searching, and moreover allows the data owner to authorize which clients could search or access the documents in the cloud. Technically, we revisit searchable symmetric encryption primitive by non-trivially combining it with a novel access control technique, and build an inverted index data structure for both attribute-based access control and sub-linear search process. Furthermore, we introduce a formalized security definition for the system, and prove its security in the simulation-based security model. Finally, we conduct a couple of experiments over a representative real-world dataset to show practicality.

The publish/subscribe (P/S) service on Advanced Metering Infrastructure (AMI) servers of smart grid need to deal with huge amount of data, which may lead to data burst on AMI servers and serious server crash. Moreover, for protecting data security, sensitive data must be encrypted before being published. It obstacles traditional data utilisation based on plaintext P/S service. Thus, enabling an encrypted data‐based P/S service is of paramount importance. Considering the huge amount of data and subscribers, it is necessary to allow conjunctive subscriptions containing mixtures of keywords, numeric data etc., and return data according to a reasonable access control mechanism (ACM). In this study, the authors propose a cloud‐assisted secure conjunctive publish/subscribe protocol to challenge the encrypted data‐based P/S service on AMI servers. To overcome the data burst, the P/S computation tasks are shifted from AMI servers to the cloud. To support conjunctive P/S operations in a reasonable ACM, a prefix‐based membership verification algorithm combining with the ciphertext policy attribute‐based encryption is explored. The proposed protocol is proved to be secure against chosen keyword/plaintext attacks under formally defined security models. Experiments on the real‐world data set further show proposed protocol indeed introduce low overhead on computation and communication.

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.

As a prominent early instance of the Internet of Things in the smart grid, the advanced metering infrastructure (AMI) provides real-time information from smart meters to both grid operators and customers, exploiting the full potential of demand response. However, the newly collected information without security protection can be maliciously altered and result in huge loss. In this paper, we propose an energy theft detection scheme with energy privacy preservation in the smart grid. Especially, we use combined convolutional neural networks (CNNs) to detect abnormal behavior of the metering data from a long-period pattern observation. In addition, we employ Paillier algorithm to protect the energy privacy. In other words, the users' energy data are securely protected in the transmission and the data disclosure is minimized. Our security analysis demonstrates that in our scheme data privacy and authentication are both achieved. Experimental results illustrate that our modified CNN model can effectively detect abnormal behaviors at an accuracy up to 92.67%.

Homomorphic encryption always allows the linear arithmetic operations performed over the ciphertext and then returns equaling results as if the operations are taken over the original plaintext, which is always used for data aggregation in wireless sensor networks to keep the confidentiality of the data and cut down the transmission overhead of the ciphertext. In the marine sensor networks, sensors collect the multiple data such as temperature, salinity, pressure, and chlorophyll concentration in the ocean using a single hardware unit for further statistical analysis such as computing the mean and the variance and making regression analysis. However, directly using the homomorphic encryption cannot perform well in marine sensor data forwarding since the data need to turn to satellites or vessels as relays and be forwarded in multi-hop way. The data are not expected to be decrypted until arriving the final destinations. To tackle these issues, we design a secure data forwarding protocol based on the Paillier homomorphic encryption and multi-use proxy re-encryption. We also evaluate the computational overhead in term of the delay in the transmission and operation in various test beds. The experiment results show that the additional computational overhead brought by cryptographic operations could be minor and it has the merit of providing fixed data size passing through the multi-hop transmission.

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 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.

Since the technology of mobile cloud computing has brought a lot of benefits to information world, many applications in mobile devices based on cloud have emerged and boomed in the last years. According to the storage limitation, data owners would like to upload and further share the data through the cloud. Due to the safety requirements, mobile data owners are requested to provide credentials such as authentication tags along with the data. However, it is impossible to require mobile data owners to provide every authenticated computational results. The solution that signers’ privilege is outsourced to the cloud would be a promising way. To solve this problem, we propose three secure multi-entities delegated authentication protocols (MEDAPs) in mobile cloud computing, which enables the multiple mobile data owners to authorize a group designated cloud servers with the signing rights. The security of MEDAPs is constructed on three cryptographic primitive identity-based multi-proxy signature (IBMPS), identity-based proxy multi-signature (IBPMS), and identity-based multi-proxy multi-signature (IBMPMS), relied on the cubic residues, equaling to the integer factorization assumption. We also give the formal security proof under adaptively chosen message attacks and chosen identity/warrant attacks. Furthermore,compared with the pairing based protocol, MEDAPs are quite efficient and the communication overhead is nearly not a linear growth with the number of cloud servers. Copyright⃝c 2015 John Wiley & Sons, Ltd.