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To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers’ data are computed and transmitted without complicated encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers’ perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads. [ABSTRACT FROM AUTHOR]

A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance $r$ from the user’s record $x$. The user’s privacy at each server is given by the fraction of the record $x$ that is kept private. In this paper, this research is initiated and protocols that offer trade-offs between privacy, computational complexity, and storage are studied. In particular, we assume that each server stores a copy of the database and study the required minimum number of servers by our protocol which provides a given privacy level. Each server receives a query in the protocol and the set of queries forms a code. The main focus in the paper is dedicated to studying the family of codes generated by the set of queries. These codes will be shown to satisfy a specific covering property and will be called private proximity retrieval intersection covering codes. In particular, since the query every server receives is a codeword, the goal is to minimize the number of codewords in such a code which is the minimum number of servers required by the protocol. These codes are closely related to a family of codes known as covering designs. We introduce several lower bounds on the sizes of such codes as well as several constructions. This work focuses on the case when the records are binary vectors together with the Hamming distance. Other metrics such as the Johnson metric are also investigated. [ABSTRACT FROM AUTHOR]

The increasing amount of graph-structured data catalyzes analytics over graph databases using semantic queries. Motivated by the ubiquity of commercial cloud platforms, data owners are willing to store their graph databases remotely. However, data privacy has emerged as a widespread concern since the cloud platforms are not fully trusted. One viable solution is to encrypt sensitive data before outsourcing, which inevitably hinders data retrieval. To enable queries over encrypted data, searchable symmetric encryption (SSE) has been introduced. Yet, the most well-studied class of SSE schemes focuses on retrieving textual files given keywords, which cannot be applied to graph databases directly. This paper extends our preliminary work (FC’17) and proposes GraphShield, a structured encryption scheme for graphs. Beyond shortest distance queries, GraphShield can support other classic graph-based queries (e.g., maximum flow) and more complicated analytics (e.g., PageRank). Technically, we incorporate a suite of (efficient) cryptographic primitives and tailor some extra secure protocols for facilitating graph analytics. Our scheme also allows updates on the encrypted graph with forward privacy guaranteed. We formalize the security model and prove the adaptive security with reasonable leakage. Finally, we implement our scheme on various real-world datasets, and the experiment results demonstrate its practicality and scalability. [ABSTRACT FROM AUTHOR]

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely, privacy and anonymity, in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal. [ABSTRACT FROM AUTHOR]

Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additive masking, require all users to quantize their model updates to the same level of quantization. This severely degrades their performance due to lack of adaptation to available communication resources, e.g., bandwidth, at different users. As the main contribution of our paper, we propose HeteroSAg, a scheme that allows secure model aggregation while using heterogeneous quantization. HeteroSAg enables the edge users to adjust their quantization proportional to their available communication resources, which can provide a substantially better trade-off between the accuracy of training and the communication time. Our proposed scheme is based on a grouping strategy by partitioning the network into groups, and partitioning the local model updates of users into segments. Instead of applying aggregation protocol to the entire local model update vector, it is applied on segments with specific coordination between users. We further demonstrate how HeteroSAg can enable Byzantine robustness while achieving secure aggregation simultaneously. Finally, we prove the convergence guarantees of HeteroSAg under heterogeneous quantization in the non-Byzantine scenario. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) underpins most intelligent transportation systems and given the open nature of the wireless communication environment, edge nodes in the VANET are subject to varying levels of attacks. While there are a large number of conditional privacy-preserving authentication protocols in the literature, most of these protocols (whose security is guaranteed by classical cryptographic primitives) are not designed to resist quantum attacks. In this paper, we propose a lattice-based conditional privacy-preserving authentication protocol for VANETs, designed to simultaneously achieve mutual authentication and privacy protection. We also show that our protocol is secure in the random oracle model, relying on the small integer solution problem. Additionally, we demonstrate the potential utility of our protocol by evaluating its performance. [ABSTRACT FROM AUTHOR]

The so-called Internet of Vehicle (IoV) systems will interconnect numerous vehicles to communicate significant information through an Internet of Things (IoT) enabled network. It has emerged as a promising system wherein various data authentication techniques have been introduced using clumsy certificate management and Diffie-Hellman (DH) assumption. However, in the presence of quantum cryptanalysis, DH-type problems could be solved in polynomial-time. In this paper, a novel certificateless data authentication protocol is designed, enabling security features in open wireless communication in the IoV. The proposed protocol resists a quantum attack using lattice cryptography. Further, a reliable blockchain mechanism is shown to provide vehicles’ trustworthiness in batch data verification. Rigorous formal analysis shows the ability of the proposed algorithm to resist existential unforgeability against the chosen-message attack. Nonetheless, the developed protocol supports other essential security functionalities, including unlikability, conditional-traceability, anti-replay, and data authenticity. Performance analysis exhibits the simulation orchestration and shows the way the proposed protocol outperforms other related techniques in energy consumption, data computation, communication, and cryptographic key storage overheads. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc networks (VANETs) plays an important role in reducing casualties, optimizing traffic distribution and improving traffic efficiency. Nevertheless, with the rapid growth of vehicles, the existing VANETs computing capacity will not be able to guarantee the service quality due to inefficient signature verification and identification. In this paper a small-batch verification and identification scheme is presented to speed up the signature verification and the invalid signature identification. In the proposed scheme, according to the signature number and the historical signature error rate, we first build an optimization model, which transforms the problem how to divide a batch of signatures into several appropriate small-batches into an integer programming problem, and then stores the optimal batch solution table in roadside units (RSUs). Moreover, in view of the current task, RSU can automatically divide a batch of signatures into several appropriate small-batches. After then, any invalid signature in these small-batches can be quickly identified by using of the exponentiation method. Finally, by comparison with the existing schemes, the total time delay is reduced to 50% in optimal circumstances. Simulation results show that the proposed scheme can achieve fast signature verification and invalid signature identification. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) domain has seen rapid developments in recent years. UAVs have been deployed for many applications and missions like data transmission, cellular service provisioning, and computational offloading tasks etc. Yet, UAV deployment is still limited, partially owing to the security challenges it poses. UAVs are particularly vulnerable to physical capture, cloning attacks, eavesdropping, and man in the middle attacks. To address some of these security problems, this paper develops an authentication protocol for use in UAV swarms. To ensure physical security and rapid authentication, the proposed protocol uses Physical Unclonable Functions (PUFs). The protocol achieves high scalability compared to the state of the art by authenticating multiple devices at once. The proposed protocol supports dynamic topologies and multi-hop communication by using spanning tree-based traversal. It is also resistant to mobility, device tampering attack, etc., and its improvements are achieved at significantly lower communication and communication cost as compared to state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

We consider parameter estimation in distributed networks, where each sensor in the network observes an independent sample from an underlying distribution and has $k$ bits to communicate its sample to a centralized processor which computes an estimate of a desired parameter. We develop lower bounds for the minimax risk of estimating the underlying parameter for a large class of losses and distributions. Our results show that under mild regularity conditions, the communication constraint reduces the effective sample size by a factor of $d$ when $k$ is small, where $d$ is the dimension of the estimated parameter. Furthermore, this penalty reduces at most exponentially with increasing $k$ , which is the case for some models, e.g., estimating high-dimensional distributions. For other models however, we show that the sample size reduction is re-mediated only linearly with increasing $k$ , e.g. when some sub-Gaussian structure is available. We apply our results to the distributed setting with product Bernoulli model, multinomial model, Gaussian location models, and logistic regression which recover or strengthen existing results. Our approach significantly deviates from existing approaches for developing information-theoretic lower bounds for communication-efficient estimation. We circumvent the need for strong data processing inequalities used in prior work and develop a geometric approach which builds on a new representation of the communication constraint. This approach allows us to strengthen and generalize existing results with simpler and more transparent proofs. [ABSTRACT FROM AUTHOR]

In our data world, a host of not necessarily trusted controllers gather data on individual subjects. To preserve her privacy and, more generally, her informational self-determination, the individual has to be empowered by giving her agency on her own data. Maximum agency is afforded by local anonymization, that allows each individual to anonymize her own data before handing them to the data controller. Randomized response (RR) is a local anonymization approach able to yield multi-dimensional full sets of anonymized microdata that are valid for exploratory analysis and machine learning. This is so because an unbiased estimate of the distribution of the true data of individuals can be obtained from their pooled randomized data. Furthermore, RR offers rigorous privacy guarantees. The main weakness of RR is the curse of dimensionality when applied to several attributes: as the number of attributes grows, the accuracy of the estimated true data distribution quickly degrades. We propose several complementary approaches to mitigate the dimensionality problem. First, we present two basic protocols, separate RR on each attribute and joint RR for all attributes, and discuss their limitations. Then we introduce an algorithm to form clusters of attributes so that attributes in different clusters can be viewed as independent and joint RR can be performed within each cluster. After that, we introduce an adjustment algorithm for the randomized data set that repairs some of the accuracy loss due to assuming independence between attributes when using RR separately on each attribute or due to assuming independence between clusters in cluster-wise RR. We also present empirical work to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), vehicular ad hoc networks (VANETs) have gradually become a research hotspot. A number of Conditional Privacy-preserving Authentication (CPPA) schemes have been proposed to protect the security and privacy of the VANETs communication. However, most of them can only provide one function between signature and signcryption, making it difficult to meet the needs of complex application scenarios. Moreover, the majority of CPPA schemes allow a Trusted Authority (TA) to expose the malicious message's sender, but they can hardly prove the authenticity of the tracking result. Therefore, we propose a new CPPA protocol to solve these problems. In our protocol, generalized ring signcryption is introduced to provide ring signature mode and ring signcryption mode in one algorithm to satisfy the different security requirements in complex application scenarios. A convenient public verification algorithm is designed to make the tracking result provable and more credible. Furthermore, we prove that our protocol achieves confidentiality, unforgeability, and Known Session-Specific Temporary Information Security (KSSTIS). We also introduce pseudonyms in our protocol to resist the ring selection attack. The performance analysis shows that our protocol has at least 61.84% improvement in computational cost compared with several similar schemes. [ABSTRACT FROM AUTHOR]

We extend the classical problem of privacy amplification to a setting where the active adversary, Eve, is also allowed to fully corrupt the internal memory (which includes the shared randomness, and local randomness tape) of one of the honest parties, Alice and Bob, before the execution of the protocol. We require that either one of Alice or Bob detects tampering, or they agree on a shared key that is indistinguishable from the uniform distribution to Eve. We obtain the following results: 1) we give a privacy amplification protocol via low-error non-malleable two-source extractors with one source having low min-entropy. In particular, this implies the existence of such (non-efficient) protocols; 2) we show that even slight improvements to the state-of-the-art explicit non-malleable two-source extractors would lead to explicit low-error, low min-entropy two-source extractors, thereby resolving a long-standing open question. This suggests that obtaining (information-theoretically secure) explicit non-malleable two-source extractors for (1) might be hard; 3) we present explicit constructions of low-error, low min-entropy non-malleable two-source extractors in the CRS model of (Garg, Kalai, Khurana, Eurocrypt 2020), assuming either the quasi-polynomial hardness of DDH or the existence of nearly-optimal collision-resistant hash functions; 4) we instantiate our privacy amplification protocol with the above mentioned non-malleable two-source extractors in the CRS model, leading to explicit, computationally-secure protocols. This is not immediate from (1) because in the computational setting we need to make sure that, in particular, all randomness sources remain samplable throughout the proof. This requires upgrading the assumption of quasi-polynomial hardness of DDH to sub-exponential hardness of DDH.We emphasize that each of the first three results can be read independently. [ABSTRACT FROM AUTHOR]

This brief introduces the concept of anonymous communication into multi-agent systems and proposes a new solution to deal with the trade-off between privacy and accuracy in privacy-preserving consensus. Furthermore, mixed encryption is applied into multi-agent systems for the first time which provides a safe and high-efficient communication. Specifically, asymmetric encryption is used at the beginning of communication to guarantee the secure exchange of initial values and a new symmetric encryption approach, called state difference, is designed and used in the following process to reduce the high cost of computing and communicating in asymmetric encryption. Combining the above methods, anonymous mixed encryption communication protocol for multi-agent systems to reach privacy-preserving consensus is finally proposed. The numerical simulation shows the effectiveness of the proposed communication protocol. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Network (VANET) is vital for supporting intelligent transport systems, such as traffic data sharing and cooperative processing in the modern city. However, data security and privacy are the critical factors restricting the development. To address these challenges, several certificateless conditional privacy-preserving authentication (CPPA) schemes with anonymity and traceability have been proposed. These schemes avoid complicated certificate management in the PKI framework and key escrow in the ID-based protocol. However, there still exist drawbacks such as computational complexity, high communication cost or security vulnerability. Recently, Ali et al. proposed an efficient certificateless CPPA (CLCPPA) scheme for VANETs, but we have found that this scheme fails to resist a signature forgery attack. To achieve a trade-off between security and efficiency, we first demonstrate the insecurity of Ali et al.’s protocol and then introduce a security-enhanced solution. To show the feasibility and utility of our proposal, we perform a security analysis in the security model. Moreover, we evaluate the performance via comparing it with other existing schemes. From the comparison results, we can find that our scheme is more efficient than prior state-of-art solutions, in terms of signing (improving 66.75%), the verification (improving 33.19%) and bandwidth requirement (reducing 14.75%). Therefore, our proposal is more suitable to be applied in VANETs. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have enabled a broad spectrum of applications serving social, commercial, and military purposes. However, since UAVs use wireless communication technologies, they are highly vulnerable to security threats. Establishing trust with the base station is the most fundamental security aspect in UAV networks to mitigate these threats. However, due to a UAV’s constrained resources, deploying traditional trust establishment schemes in UAV networks becomes challenging. Further, this issue escalates as the number of UAVs increases. To address this issue, we propose an authentication cum attestation protocol for UAV swarms using an optimal communication trajectory, which can establish the required trust in a lightweight manner. Furthermore, our protocol uses Physical Unclonable Functions (PUFs) and thus guarantees physical security as well. We demonstrate that the proposed protocol is feasible, scalable, and secure using a formal Mao Boyd logic approach. Comparative analyses show that the proposed protocol outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information retrieval, cloud security, and privacy protection. While FHE schemes do not require any interaction during computation, the key limitations are large ciphertext expansion and inefficiency. Thus, to overcome these limitations, we develop a novel cryptographic tool, MAS-Encryption (MASE), to support real-value input and secure computation on the multiply-add structure. The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two case study examples. Specifically, the first case study example is the privacy-preserving Naive Bayes classifier that can achieve minimal Bayes risk, and the other example is the privacy-preserving support vector machine. We prove that the constructed classifiers are secure and evaluate their performance using real-world datasets. Experiments show that our proposed MASE scheme and MASE based classifiers are efficient, in the sense that we achieve an optimal tradeoff between computation efficiency and communication interactions. Thus, we avoid the inefficiency of FHE based paradigm. [ABSTRACT FROM AUTHOR]

Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the servers storing the database. While existing protocols strictly impose that no information is leaked on the file’s identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the perfect privacy requirement. We refer to such protocols as weakly-private information retrieval (WPIR) protocols. In particular, for the case of multiple noncolluding replicated servers, we study how the download rate, the upload cost, and the access complexity can be improved when relaxing the perfect privacy constraint. To quantify the information leakage on the requested file’s identity we consider mutual information (MI), worst-case information leakage, and maximal leakage (MaxL). We present two WPIR schemes, denoted by Scheme A and Scheme B, based on two recent PIR protocols and show that the download rate of the former can be optimized by solving a convex optimization problem. We also show that Scheme A achieves an improved download rate compared to the recently proposed scheme by Samy et al. under the so-called $\epsilon $ -privacy metric. Additionally, a family of schemes based on partitioning is presented. Moreover, we provide an information-theoretic converse bound for the maximum possible download rate for the MI and MaxL privacy metrics under a practical restriction on the alphabet size of queries and answers. For two servers and two files, the bound is tight under the MaxL metric, which settles the WPIR capacity in this particular case. Finally, we compare the performance of the proposed schemes and their gap to the converse bound. [ABSTRACT FROM AUTHOR]

We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on the tasks of goodness-of-fit testing and estimation of discrete distributions. From prior work, these tasks are well understood under noninteractive protocols. Extending these approaches directly for interactive protocols is difficult due to correlations that can build due to interactivity; in fact, gaps can be found in prior claims of tight bounds of distribution estimation using interactive protocols. We propose a new approach to handle this correlation and establish a unified method to establish lower bounds for both tasks. As an application, we obtain optimal bounds for both estimation and testing under local differential privacy and communication constraints. We also provide an example of a natural testing problem where interactivity helps. [ABSTRACT FROM AUTHOR]

Secure message transmission in vehicular communications in smart cities is still a challenging task. Most of the related work employed the Public Key Infrastructure, Certification Revocation Lists (CRLs) for ensuring security, privacy. However, these work suffered from some issues such as: 1) the time-consuming checking process, huge size of CRLs, 2) traceability attacks by linking unencrypted Basic Safety Messages (BSMs), 3) extracting secret keys from the storage of parked vehicles or road-side units (RSU) by an adversary. To address the aforementioned issues, we thus propose a physically secure privacy-preserving message authentication protocol using Physical Unclonable Function (PUF), Secret Sharing. The proposed protocol guarantees security, privacy against passive, active attacks even under memory leakage. The entities (i.e., vehicles, RSU) make use of their PUF to reconstruct a secret polynomial-share so that pairwise temporal secret keys (PTKs) can be established with other entities. Unlike existing protocols, BSMs are also encrypted in our protocol (by PTKs) to provide a higher level of security, thwart vehicles traceability attacks. To revoke a vehicle, RSU needs not broadcast CRLs. Instead, RSU distributes only a secure offset key using threshold Secret Sharing. Consequently, our revocation checking process has computation complexity $\mathcal {O}(1)$. Our protocol also eliminates the need for a third party in Vehicle-to-Vehicle communication to ensure expeditious transmission. Security analysis, performance evaluation show that our proposed protocol outperforms existing schemes in terms of security features, computation, communication cost. [ABSTRACT FROM AUTHOR]

The concept of the smart city and the extensive use of vehicles are evolving progressively. This evolution makes vehicular ad-hoc network (VANET) a widely used network for inter-vehicular communication to get information about speed, traffic congestion, road condition, and vehicle location. This information is obtained from a public network, which is prone to various attacks. Generally speaking, data security in VANET is one of the most crucial tasks. We found many authentication protocols for VANETs; however, most of these protocols are insecure due to impersonation, replay, denial of service attacks, etc. Moreover, the existing protocols utilized heavy computation and communication costs, making them incompatible for resource-constrained VANET applications. This article presents a lightweight privacy-preserving authentication protocol for VANETs to offer a secure and efficient data transmission mechanism over a public channel. We analyze that our protocol is lightweight, safe, and scalable compared to related protocols. The communication and computation cost of our protocol is less concerning the state-of-the-art protocols. Hence, it is overt from the observed facts that our protocol is more reliable, convenient, and practical for secure data transmission in VANETs. [ABSTRACT FROM AUTHOR]