Combinatorial optimization (CO) is a fundamental tool in many fields. Many neural combinatorial optimization (NCO) methods have been proposed to solve CO problems. However, existing NCO methods typically require significant computational and storage resources, and face challenges in maintaining robustness to distribution shifts between training and test data. To address these issues, we model CO instances into probability measures, and introduce Wasserstein-based metrics to quantify the difference between CO instances. We then leverage a popular data compression technique, \emph{coreset}, to construct a small-size proxy for the original large dataset. However, the time complexity of constructing a coreset is linearly dependent on the size of the dataset. Consequently, it becomes challenging when datasets are particularly large. Further, we accelerate the coreset construction by adapting it to the merge-and-reduce framework, enabling parallel computing. Additionally, we prove that our coreset is a good representation in theory. {Subsequently}, to speed up the training process for existing NCO methods, we propose an efficient training framework based on the coreset technique. We train the model on a small-size coreset rather than on the full dataset, and thus save substantial computational and storage resources. Inspired by hierarchical Gonzalez’s algorithm, our coreset method is designed to capture the diversity of the dataset, which consequently improves robustness to distribution shifts. Finally, experimental results demonstrate that our training framework not only enhances robustness to distribution shifts but also achieves better performance with reduced resource requirements.

In order to meet unprecedented demands for reliable and massive connectivity (MC), sixth-generation (6 G) cellular Radio Access Networks (RANs) require architectural innovations. Conventional cellular RANs' scalability is limited by the tightly coupled control and user planes. Fully-Decoupled RANs (FD-RANs) are a promising architectural innovation that enables flexible plane separation. However, current architectures have limitations due to ineffective multiple access schemes. To this end, we introduce NomaFdRaN, an innovative Non-Orthogonal Multiple Access (NOMA)-optimized FD-RAN architecture in order to optimize reliability and MC. To achieve holistic system optimization, NomaFdRaN applies NOMA on all network planes, control plane and user plane, and transmission paths, uplink and downlink. To improve NOMA efficiency, we develop a user pairing optimization approach that minimizes total transmit power while maintaining linear computing complexity. Based on stochastic geometry, we develop analytical models to analyze NomaFdRaN's performance. Subsequently, we analytically derived closed-form expressions for key performance metrics. Our simulation results demonstrate the effectiveness of the NomaFdRaN architecture and provide insights into the deployment strategies for next-generation FD-RANs.

In 5G Radio Access Networks (RAN), Cyclic Redundancy Check (CRC) algorithms play a vital role in detecting accidental changes to digital data during transmission. However, due to the massive bandwidth demands in 5G networks, CRC computation is a resource-intensive process. To address this challenge, we propose performing CRC computation and verification directly in the network path. Specifically, we introduce NetCRC-NR, a 5G New Radio (NR) standard-compliant in-network CRC accelerator. NetCRC-NR implements the 5G NR CRC algorithms specified in 3GPP TS 38.212, including CRC24A, CRC24B, CRC24C, CRC16, CRC11, and CRC6. It leverages programmable switches to perform in-network CRC generation and validation for the Transport Blocks (TBs) and Code Blocks (CBs), aiming at providing high CRC computation throughput and alleviating the computational burden on General-Purpose Processors (GPPs). We design and implement NetCRC-NR on Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Extensive experiments demonstrate that NetCRC-NR performs CRC generation and verification at the switch line rate of up to 4+Tbps CRC throughput, showcasing its efficiency and potential in accelerating the 5G RAN error detection process.

Self-healing group key distribution (SGKD) protocols guarantee the security of group communications by allowing authorized users to independently recover missed previous session keys from the current broadcast without retransmission. However, existing SGKD protocols have flaws: (1) collusion resistance and revocable nodes are both upper-bounded by the degree of polynomials used, (2) the disclosure of personal secrets enables the recovery of group key, (3) temporary revocation of a group member is not possible, and (4) a revoked node may obtain the session key when initiating mutual healing, moreover, a malicious node may cause the recovery of false group keys. To address these limitations, we propose an SGKD protocol using the Chinese remainder theorem (CRT) and Physical Unclonable Function (PUF). Our proposed SGKD protocol generates a PUF-based dynamic secret by stimulating nodes’ PUF using a polynomial-based encrypted challenge. This secret is then employed to retrieve a CRT-based encrypted group key. By combining PUF and CRT, we can generate dynamic secrets on the fly and reduce computation time significantly. Utilizing such a technique, our protocol achieves superior security goals, including resistance to any coalition of group nodes even if nodes’ personal secrets were disclosed. Furthermore, the proposed protocol provides an unlimited number of revocable nodes. Additionally, a revoked node can rejoin its group in later sessions without affecting backward secrecy. Moreover, the protocol provides a backward secrecy guaranteed mutual-healing feature free from desynchronization. Our performance and security analyses (i.e., theorem-based formal analysis, NS3-based experiment, and formal verification using the AVIPSA tool) show that our proposed protocol achieves stronger security goals and better efficiency in terms of computation, communication, and storage costs compared to existing SGKD schemes.

In wireless sensor networks (WSNs), energy efficiency, reliability, and non-plaintext transmission of the sensed data are major concerns, and all three of them are indispensable. Based on robust Chinese Remainder Theorem (RCRT), this paper proposes an improved data aggregation scheme to satisfy the requirements of energy efficiency, reliability, and non-plaintext transmission simultaneously. Compared with the existing RCRT-based data aggregation scheme, our improved RCRT-based data aggregation scheme tolerates an unrestricted error at the expense of certain energy saving.

In wireless sensor networks (WSNs), to improve sensing accuracy and coverage, a large number of sensor nodes are usually deployed in the monitoring area. The high density makes the same physical quantity sensed by adjacent sensor nodes the same or similar due to measurement errors, causing a lot of data redundancy and energy waste. In addition, reliability and non-plaintext transmission of the sensed data are also major concerns in WSNs. This paper proposes an improved data aggregation scheme to satisfy the requirements of energy efficiency, reliability, and non-plaintext transmission simultaneously. Based on robust Chinese Remainder Theorem (RCRT), the scheme not only compresses the data when it is sensed but also obtains the approximate measurement result even if any number of small measurement errors and an unrestricted measurement error occur in sensed data. Compared with the existing RCRT-based data aggregation scheme, our RCRT-based data aggregation scheme tolerates an unrestricted error at the expense of certain energy saving.

In wireless sensor networks (WSNs), to improve sensing accuracy and coverage, a large number of sensor nodes are usually deployed in the monitoring area. The high density makes the same physical quantity sensed by adjacent sensor nodes the same or similar due to measurement errors, causing a lot of data redundancy and energy waste. In addition, reliability and non-plaintext transmission of the sensed data are also major concerns in WSNs. This paper proposes a novel data aggregation scheme to satisfy the requirements of energy efficiency, reliability, and non-plaintext transmission simultaneously. Based on a Residue Number System with Nonpairwise-Prime Moduli (RNS-NPM), named D-RNS-NPM, the scheme not only compresses the data when it is sensed but also obtains the approximate measurement result even if any number of small measurement errors and a bounded number of unrestricted measurement errors occur in sensed data. Compared with the data aggregation scheme based on robust Chinese Remainder Theorem (RCRT), our D-RNS-NPM-based data aggregation scheme tolerates unrestricted errors at the expense of certain energy saving.

Abstract To ensure private message exchange among the group members, it is desirable to construct secure and efficient group key management schemes. Moreover, these schemes are more versatile if they could support dynamic join or leave of group members. In IET Information Security 2014, Vijayakumar et al. have introduced such a group key management scheme with lightweight overheads in both computation and communication. And this scheme has been used as a building block in many cryptographic protocols afterwards. In this paper, the authors demonstrate that Vijayakumar's scheme suffers some potential security weaknesses. First, after participating in the group communications for some sessions, a group member may still be able to obtain the group key after it leaves the group, and this violates the claimed security property of forward secrecy. Second, some colluding group members may derive another group member's long term secret key, and obviously, this has more serious consequences. One of the main reasons for the existence of these attacks is that the security analyses in Vijayakumar's scheme are informal and they cannot cover the dynamic environment. To address this issue, the authors’ suggestion is that heuristic arguments of security are not adequate in the design of cryptographic protocols, but formal security definitions and proofs are required.

In wireless sensor networks (WSNs), to improve sensing accuracy and coverage, a large number of sensor nodes are usually deployed in the monitoring area. The high density makes the data sensed by adjacent sensor nodes the same or similar, causing a lot of data redundancy and energy waste. In addition, reliability and non-plaintext transmission of the sensed data are also major concerns in WSNs. In this paper, we propose a novel data aggregation scheme to satisfy the requirements of energy efficiency, reliability, and non-plaintext transmission simultaneously, which obtains the approximate measurement result when small measurement errors are allowed. The scheme employs robust Chinese Remainder Theorem (RCRT) to compress the data when it is sensed and no other assumptions are required. We further derive some analytical results and give the simulation results of our scheme. Finally, we compare the performance of the typical data aggregation schemes with our RCRT-based data aggregation scheme in experimental simulation. The results demonstrate that the proposed RCRT-based data aggregation scheme has a better performance in energy saving.

In a t , n threshold secret sharing (SS) scheme, whether or not a shareholder set is an authorized set totally depends on the number of shareholders in the set. When the access structure is not threshold, (t,n) threshold SS is not suitable. This paper proposes a new kind of SS named grouped secret sharing (GSS), which is specific multipartite SS. Moreover, in order to implement GSS, we utilize both Lagrange interpolation polynomials and Chinese remainder theorem to design two GSS schemes, respectively. Detailed analysis shows that both GSS schemes are correct and perfect, which means any authorized set can recover the secret while an unauthorized set cannot get any information about the secret.

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for the security analysts of various companies to unveil the underlying maliciousness within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help security analysts understand how the target apps worked and facilitate the identification of the maliciousness within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graphs (CFGs). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying the maliciousness of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the real device in the experiments.

This paper focuses on constructions of MDS self-dual codes from (extended) generalized Reed-Solomon (GRS) codes. Let$q = r^{2}$be an odd prime power. We show that, there exists a$q$-ary self-dual (extended) GRS code for each even length in the range$[{2r,3r-3}]$, and for each singly even length in the range$[3r-1,4r]$. This extends the only known consecutive range$[2,2r]$to$[{2,3r-3}]$for this case. Furthermore, our general constructions provide many MDS self-dual codes with new parameters which, to the best of our knowledge, were not reported before.

Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.

We propose and implement a novel positioning system, WalkieLokie, which directly calculates the relative position from a smart device to a target. The requirement of the target is simple: it is attached with a “dummy” acoustic speaker, which does not have any other rich capabilities, such as audio recording, communication, or computation. Hence, the proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. WalkieLokie leverages the walking motion for locating an acoustic speaker. The key insight is that the distance between the user and the speaker varies in real time when the user walks, and the pattern of the variance implies the relative position. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean errors of ranging and direction estimation are 0.63 m and 2.46°, respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

In this paper, we propose and implement WalkieLokie, a novel acoustic-based relative positioning system. WalkieLokie facilitates a multitude of Augmented Reality (AR) applications: users with smart devices can passively acquire surrounding information in real time, similar to the commercial AR system Wikitude; the surrounding presenters, who want to share information or introduce themselves, can actively launch the function on demand. The key rational of WalkieLokie is that a user can perceive a series of spatial-related acoustic signals emitted from a presenter, which depicts the relation position between the user and the presenter. The proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean error of ranging and direction estimation is 0.63m and 2.46 degrees respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

A basic (t,n)-secret sharing (SS) scheme allows a secret s to be divided into n shares and shared among n shareholders. In the scheme, any t or more than t shareholders can recover the secret while fewer than t shareholders cannot obtain the secret s. But an adversary without any valid share may obtain the secret if there are over t participants in the secret reconstruction. To address this type of attack, we first introduce the notion of randomized component (RC), which binds a share with all participants and protects the share from being exposed to outside without any computational assumption; at the same time, RCs can be used to reconstruct the secret. As one of the applications of RCs, a (t,m,n)-group oriented SS scheme is proposed to cope with the attack in basic (t,n)-SSs, in which once m (m ≥ t) participants form a tightly couple group by generating RCs, the secret can be recovered only if all m RCs are correct, which requires each participant to have a valid share in advance. Moreover, the scheme can secure the secret without any user authentication or share verification. Analyses show the proposed (t,m,n)-group oriented SS is asymptotically perfect and unconditionally secure. RCs can also be applied to build other schemes in a simple way, such as multi-SS, group authentication, and so on.

ABSTRACT A (t,n) secret sharing scheme (SS) enables a dealer to divide a secret into n shares in such a way that (i) the secret can be recovered successfully with t or more than t shares, and (ii) the secret cannot be recovered with fewer than t shares. A verifiable secret sharing scheme (VSS) has been proposed to allow shareholders to verify that their shares are generated by the dealer consistently without compromising the secrecy of both shares and the secret. So far, there is only one secure Chinese remainder theorem‐based VSS using the RSA assumption. We propose a Chinese remainder theorem‐based VSS scheme without making any computational assumptions, which is a simple extension of Azimuth–Bloom (t,n) SS. Just like the most well‐known Shamir's SS, the proposed VSS is unconditionally secure. We use a linear combination of both the secret and the verification secret to protect the secrecy of both the secret and shares in the verification. In addition, we show that no information is leaked when there are fewer than t shares in the secret reconstruction. Copyright © 2013 John Wiley & Sons, Ltd.

Trusted Platform Module (TPM) is a coprocessor for detecting platform integrity and attesting the integrity to the remote entity. There are two obstacles in the application of TPM: minimizing trusted computing base (TCB) for reducing risk of flaws in TCB, for which a number of convincing solutions have been developed; formal guarantees on each level of TCB, where the formal methods on analyzing the application level have not been well addressed. To the best of our knowledge, there is no general formal framework for developing the TPM-based protocol applications, which not only guarantees the security but also makes it easier for design. In this paper, we make fine-grained refinement on TPM-based security protocols to illustrate our formal solution on the application level by using the Event-B language. First, we modify the classical Dolev-Yao attacker model, which assumes normal entity's compliance with the protocol even without TPM's protection. Thus, the classical security protocols are vulnerable in this modified attacker model. Second, we make stepwise refinement of the security protocol by refining the protocol events and adding security constraints. From the fifth refinement, we make a case study to illustrate the entire refinement and further formally prove the key agreement protocol from DAAODV, the TPM-based routing protocol, under the extended Dolev-Yao attacker model. The refinement provides another way of formal modeling the TPM-based security protocols and a more fine-grained model to satisfy with the rigorous security requirement of applying TPM. Finally, we prove all the proof obligations generated by Rodin, an Eclipse-based IDE for Event-B, to ensure the soundness of our proposal.

With the rapid development of MANET, the secure and practical authentication problem in it increasingly becomes outstanding. The existing work study the problem from two aspects, i.e. secure key division/distributed storage and secure distributed authentication. But existing cheating problems and fault attack possibility will break the security. Besides, efficiency performance of such schemes is not good enough due to the exponential arithmetic with Shamir's scheme. Due to these problems above, we explore the property of verifiable secret sharing(VSS) schemes with Chinese Remainder Theorem(CRT). Then a secret key distributed storage scheme based on CRT-VSS and trusted computing is proposed for MANET. We utilize trusted computing technology to solve two existing cheating problems in secret sharing area before. After that we do some analysis of the homomorphism property with CRT-VSS scheme. Compared with the secure shares-product sharing scheme based on Shamir's scheme, we design the corresponding scheme base on CRT-VSS scheme with better concision and equal security later. On such basis, a distributed Elliptic Curve-Digital Signature Standard signature (ECC-DSS) authentication scheme based on CRT-VSS scheme and trusted computing is proposed. The choice of the trusted authentication node can eliminates the possibility of traditional DoS and fault attack. At last, we do some security analysis towards our schemes proposed above.