Privacy-preserving Protocol Research Articles

Lie hypergraph and chaos‐based privacy preserving protocol for wireless sensor networks in IoT environment

SummaryWireless sensor networks (WSN) are innately resource restrained and beneficial in a wide range of applications, including smart homes, e‐health care, law, military systems, disaster management, and emergency reprieve. These applications are linked to various devices that may communicate with one another through the internet, typically known as Internet of Things (IoT). The application of WSN plays an integral role in the IoT infrastructure. In a WSN, sensors are haphazardly placed in environments where the data transmission is challenged by privacy concerns. This paper proposes a methodology termed Lie hypergraph and chaos‐based secure routing (LH‐CSR) to perform an energy‐efficient routing with secure data transmission in WSN. In the first phase, the deployed sensor nodes are transformed into a hypergraph from which cluster head (CH) is elected by hypergraph transversal property, and for secure routing, the route is formed by the Lie commutators of the Lie algebra of upper triangular matrices. The second phase emphasizes privacy preservation by introducing the novel chaotic map formulation to process the key generation. The encryption and decryption processes are maintained by key generation to prevent data loss during retrieval. In this way, the data are retained confidentially with minimal computational overhead. The performance of the LH‐CSR is evaluated through a simulation, which shows that it outperforms over compared protocols in terms of cryptographic time, network lifetime, packet delivery ratio, end‐to‐end delay, and throughput.

Towards Efficient and Privacy-Preserving High-Dimensional Range Query in Cloud

The Internet of Things (IoT) boom has enabled Internet Service Providers (ISPs) to collect an enormous amount of high-dimensional data. Performing range queries on such data can effectively reuse them to help ISPs offer better services. Owing to the low cost and high resource utilization of cloud computing, an increasing number of ISPs are inclined to outsource data and services to it. However, as the cloud is not fully trusted, data need to be encrypted before being outsourced, which inevitably hinders many query services, e.g., range queries. Various schemes were proposed for privacy-preserving range queries, yet they struggled to extend to high-dimensional scenarios and did not support dimension selection. Aiming at this challenge, in this paper, we propose an efficient and privacy-preserving high-dimensional range query scheme (PHRQ) based on an iMinMax tree while supporting dimension selection. Specifically, we first build an iMinMax tree for high-dimensional data and utilize a symmetric homomorphic encryption technique to design a suite of privacy-preserving protocols to achieve secure high-dimensional range queries. Then, we design a sub-dimensional range determination protocol to support dimension selection. Further, based on the iMinMax tree and our privacy-preserving protocols, we propose our PHRQ scheme. Finally, security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is efficient in high-dimensional range query processing.

Privacy-preserving continuous authentication using behavioral biometrics

Continuous authentication modalities collect and utilize users’ sensitive data to authenticate them continuously. Such data contain information about user activities, behaviors, and other demographic information, which causes privacy concerns. In this paper, we propose two privacy-preserving protocols that enable continuous authentication while preventing the disclosure of user-sensitive information to an authentication server. We utilize homomorphic cryptographic primitives that protect the privacy of biometric features with an oblivious transfer protocol that enables privacy-preserving information retrieval. We performed the biometric evaluation of the proposed protocols on two datasets, a swipe gesture dataset and a keystroke dynamics dataset. The biometric evaluation shows that the protocols have very good performance. The execution time of the protocols is measured by considering continuous authentication using: only swipe gestures, keystroke dynamics, and hybrid modalities. The execution time proves the protocols are very efficient, even on high-security levels.

A quantum resistant multi-user signcryption scheme featuring key invisibility for Internet of Things

With the advent of ubiquitous computing, multiple heterogeneous devices have evolved with increased connectivity, yielding the Internet of Things (IoT). For establishing secure communication, IoT has effectively become a primary security concern. The massive connectivity of the resource-constrained devices in IoT and enormous data on the air have made information vulnerable to several types of attacks. Although several resource-intensive cryptographic algorithms have been proposed to combat this security threat, signcryption has been identified as a sound solution for such an application.In this work, we propose an isogeny-based signcryption scheme by integrating HyPKE, an isogeny-based encryption scheme with an isogeny-based signature SeaSign based on Fiat Shamir with aborts. Apart from confidentiality and authenticity requirements, our scheme also achieves ciphertext anonymity and key invisibility, unlike the existing signcryption schemes in the post-quantum world. More positively, our security model considers insider attacks, supports multi-users setting and offers non-repudiation. Our work also studies the key privacy notion of the isogeny-based encryption scheme Hashed-PKE, which can be of independent interest in designing several anonymous channels or privacy-preserving protocols. More precisely, we showed Hashed-PKE achieves indistinguishability of keys against chosen-plaintext attack. This leads to the indistinguishability of keys against chosen-ciphertext attack security of HyPKE, a hybrid encryption scheme that is obtained by applying the Fujisaki Okamoto transformation to Hashed-PKE. We have proved our result in the standard model assuming the hardness of the Commutative Supersingular Decisional Diffie–Hellman assumption.

Privacy-Preserving Face Recognition With Multi-Edge Assistance for Intelligent Security Systems

Face recognition is one of the key technologies in intelligent security systems. Data privacy and identification efficiency have always been concerns about face recognition. Existing privacy-preserving protocols only focus on the training phase of face recognition. Since intelligent security systems mainly to complete the calculation of large-scale face data in identification phase, existing privacy-preserving protocols cannot be well applied to intelligent security systems. In this paper, we propose the first privacy-preserving face recognition protocol for the calculations in the identification phase for intelligent security systems. We introduce the Householder matrix to blind user data including model data and face data, which enables the proposed protocol to support privacy-preserving face recognition on semi-trusted edge servers. Utilizing edge computing, fast response for large-scale face recognition can be achieved. The user can offload heavy calculations of matrix multiplication and Euclidean distances to edge servers simultaneously. The proposed protocol supports parallel computing based on multiple edge servers and thus enhances the efficiency of face recognition in intelligent security systems. Moreover, the recognition accuracy in the proposed protocol is the same as that in the original PCA-based face recognition algorithm. The security analysis demonstrates that the protocol protects the privacy of user data. The numerical analysis and simulation experiments are carried out to show the efficiency and feasibility of the proposed protocol.

Cryptographic protocol for privacy-preserving integration of HAZOPs in modular process plants

Information which is contained in Hazard & Operability (HAZOP) studies is highly sensitive since it can reveal the vulnerabilities of a system and potential ways in which to bypass safeguards. Through the design of systems involving collaboration along a value chain, at some point this information is shared between several parties. In this paper, we propose a methodology for the secure exchange of safety information whilst preserving sensitive information for the application of modular Hazard & Operability (HAZOP) studies. We use homomorphic encryption in a workflow for the sharing of information between plant owners and operators as well as module vendors. We apply encryption to the risks from different modular HAZOPs (mHAZOPs), and combine and compare them without disclosing the risk level. Our contribution is a privacy-preserving protocol for mHAZOP comparison during the integration of modular process and equipment. We provide an exemplary implementation of the protocol and demonstrate the protocol’s privacy and correctness.

Efficient and privacy-preserving group signature for federated learning

Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users’ devices, called clients. Only the training results, called gradients, are sent to the server to be aggregated which is used to generate an updated model. However, we cannot assume that the server can be trusted with the sensitive information, such as metadata related to the owner or source of the data. So, hiding client information from the server helps in reducing privacy-related attacks. Therefore, the privacy of the client’s identity, along with the privacy of the client’s data, is necessary to prevent such attacks. This paper proposes an efficient and privacy-preserving protocol for FL based on group signatures. A new group signature for federated learning, called GSFL, is designed to not only protect the privacy of the client’s data and identity but also significantly reduce the computation and communication costs considering the iterative process of federated learning. We show that GSFL outperforms existing approaches in terms of computation, communication, and signaling costs. Also, we show that the proposed protocol can handle various security attacks in the federated learning environment. Moreover, we provide security proof of our protocol using a formal security verification tool, Automated Validation of Internet Security Protocols and Applications (AVISPA).

P2AE: Preserving Privacy, Accuracy, and Efficiency in Location-Dependent Mobile Crowdsensing

With the widespread prevalence of smart devices, mobile crowdsensing (MCS) becomes a new trend to encourage mobile nodes to participate in cooperative data collection in various Internet of Things (IoT) applications. In location-dependent MCS, location information of mobile nodes are collected and analyzed by service provider to assist in task allocation. If the service provider is not fully trusted, mobile node's privacy is leaked and accessed by unauthorized parties. How to preserve privacy while maintaining task allocation accuracy and efficiency becomes challenging. To this end, we propose a learning-based mechanism that involves two parts: 1) privacy-preserving task release and task allocation; 2) accurate and efficient task allocation. In the first part, we design a location-based symmetric key generator, which enables two parties to self-generate a symmetric key without depending on fully trusted authorities. By utilizing this key generator and Proxy Re-encryption, we propose a privacy preserving protocol to protect location information in task release and task allocation. In the second part, we design a reinforcement learning based task allocation algorithm to optimize the winners selection, which obtains high accuracy and efficiency. The performance analysis reveals that our proposed mechanism achieves accurate and efficient task allocation while preserving privacy in location-dependent MCS.

Event-Trigger-Based Finite-Time Privacy-Preserving Formation Control for Multi-UAV System

Privacy-preserving has been crucial technique of multi-UAV systems, including cooperative detection, cooperative penetration and strike. Unprocessed interactive information poses a serious privacy threat to UAV swarm collaborative tasks. Considering not only privacy-preserving but also bandwidth constraints and the convergence performance of multi-UAV systems, this paper comprehensively proposes an original event-triggered-based finite-time privacy-preserving formation control scheme to resolve these three factors. Firstly, this paper adopted a local, deterministic, time-varying output mapping function for a privacy mask, which encodes the internal states of the UAV prior to its public transmission, and the initial true value of each UAV’s states is kept indecipherable for honest-but-curious UAVs and other malicious eavesdropping attackers. Then, considering the limited communication bandwidth and channels, we employed a distributed event-triggered strategy and deduced the triggering condition for consensus-based formation control, which effectively reduces the excessive consumption of communication and computational resources in contrast to time-triggered strategy. In terms of the convergence performance of the UAVs, finite-time stability theory was introduced to make the system reach the desired formation in finite time and obtain a settling time related to the initial state. Compared with the existing literature, this paper systematically took into account the above three factors for multi-UAV systems and provides a convergence analysis and a privacy analysis in detail. Finally, the effectiveness of the finite-time privacy-preserving protocol based on an event-triggered strategy was demonstrated by numerical simulation examples and comparative experiments. The proposed method achieves the formation control under privacy-preserving, improves the convergence rate and reduces the frequency of controller updates and information transmission.

Compressed Zero-Knowledge Proofs for Lattice-Based Accumulator

Abstract The lattice-based cryptographic accumulators, which enable short zero-knowledge arguments of membership, have numerous applications in post-quantum privacy-preserving protocols. However, most efficient quantum-safe zero-knowledge arguments are PCP-based systems and rely on non-falsifiable assumptions. For non-PCP-based constructions using the state-of-the-art techniques on compressing lattice-based zero-knowledge proofs, the concrete size of the resulting proof for accumulators with $2^{32}$ members is at least 500 KB. In this paper, we propose a compact non-PCP zero-knowledge proof for the lattice-based Merkle-tree, which leads to an efficient post-quantum cryptographic accumulator. The complexity of our construction is logarithmic in $l\cdot n_{s}$, where $l$ and $n_{s}$ denote the depth of the underlying Merkle-tree and the size of a node, respectively, and the concrete size is only $143.7\ $KB when $l=32$. In particular, we provide an improved lattice-based Bulletproof with efficient knowledge extraction, which allows large challenge space but small soundness slack. Furthermore, the amortized technique can be applied to the Bulletproof without breaking the knowledge soundness due to our improved knowledge extraction. As a direct application, we present a practical lattice-based ring signature, which can achieve logarithmical signing/verifying computational complexity with the number of the ring, while the state-of-the-art constructions (CRYPTO 21) have linear computational complexity.

Distributed Event-Triggered Algorithms for Finite-Time Privacy-Preserving Quantized Average Consensus

In this article, we consider the problem of privacy preservation in the average consensus problem when communication among nodes is quantized. More specifically, we consider a setting where nodes in the network can be curious, while certain nodes in the network want to ensure that their initial states cannot be inferred exactly by these curious nodes. Curious nodes are not malicious, i.e., they try to identify the initial states of other nodes based on the data they receive during their operation (and some of them might even collude) but do not interfere in the computation in any other way. Each node in the network (including curious nodes) can opt to execute a privacy-preserving algorithm (so as not to reveal the initial state it contributes to the average calculation) or its underlying (plain) average consensus algorithm (if privacy is not a concern). We propose two privacy-preserving event-triggered quantized average consensus algorithms. Under certain topological conditions, both the proposed algorithms allow the nodes that adopt privacy-preserving protocols to preserve their privacy and at the same time to obtain, after a finite number of steps, the exact average of the initial states while processing and transmitting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantized</i> information. We also present illustrative examples and comparisons of our algorithms against other algorithms in the existing literature and discuss a motivating application in which smart meters in a smart grid collect real-time demands in a privacy-preserving manner.

SecureRC: A system for privacy-preserving relation classification using secure multi-party computation

Natural Language Processing (NLP) transforms human language into machine language that can be understood by machines through computer technology. Relation classification is an important semantic extraction task in NLP, which can accurately obtain the semantic relationship between two entities in a text. It has been used extensively in many NLP applications, such as information extraction and question answering. Relation classification can extract accurately relationships between two entities from large amounts of linguistic data. However, collecting such vast amounts of data in relation classification poses serious privacy issues. As far as we know, there is no existing work that implements privacy-preserving relation classification task. In this paper, we consider how to implement privacy-preserving relation classification using attention-based gated recurrent unit (GRU) network. Specifically, we first design three basic privacy-preserving protocols for the non-linear functions (sigmoid and tanh) using secure multi-party computation (MPC). Then, we propose a secure computation protocol SecureGRU for the GRU network based on these three basic protocols. Finally, based on the SecureGRU and the attention mechanism, we obtain the privacy-preserving relation classification system SecureRC. In the semi-honest adversary model, we prove the security of these protocols. Any honest-but-curious adversary is not able to obtain anything beyond what he is allowed to learn. The proposed system is implemented in Python. Experimental results demonstrate the performance of our proposed protocols.

An Extensive Study on Lattice-Based Cryptography and its Applications for RLWE-Based Problems

Lattice-based cryptography has emerged as a powerful paradigm for constructing secure cryptographic primitives, offering resistance to quantum attacks and providing a versatile framework for building post-quantum cryptographic systems. This research paper provides an in-depth exploration of lattice-based cryptography, focusing specifically on its applications for problems based on Ring Learning with Errors (RLWE). We analyze the fundamental concepts of lattice theory, delve into the RLWE problem, and highlight the security properties and challenges associated with lattice-based schemes. Furthermore, we discuss various real-world applications of lattice-based cryptography, demonstrating its potential for secure communication, privacy-preserving protocols, and post-quantum cryptography.

Privacy-Aware Secure Region-Based Handover for Small Cell Networks in 5G-Enabled Mobile Communication

The 5G mobile communication network provides seamless communication between users and service providers and promises to achieve several stringent requirements, such as seamless mobility and massive connectivity. Although 5G can offer numerous benefits, security and privacy issues still need to be addressed. For example, the inclusion of small cell networks (SCN) into 5G brings the network closer to the connected users, providing a better quality of services (QoS), resulting in a significant increase in the number of Handover procedures (HO), which will affect the security, latency and efficiency of the network. It is then crucial to design a scheme that supports seamless handovers through a secure authentication process. With this aim, in this article, we propose a secure region-based handover scheme that supports seamless connectivity for SCNs in 5G. Our proposed scheme is based on asymmetric-key-based authenticated key exchange and handover protocols that preserve user privacy and network security while providing a seamless region-based handover mechanism and effective membership revocation management. In this context, we introduce three privacy-preserving protocols, i.e., an initial authentication protocol, an intra-region handover protocol and an inter-region handover protocol, for dealing with three communication scenarios. To the best of our knowledge, this is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> paper to consider the privacy and security in both the intra-region and inter-region handover scenarios in 5G communication with effective membership revocation management support. Detailed security and performance analysis of our proposed scheme is presented to show that it is resilient against many security threats, is cost-effective and provides an efficient solution for 5G-enabled mobile communication.

A General Purpose Data and Query Privacy Preserving Protocol for Wireless Sensor Networks

A General Purpose Data and Query Privacy Preserving Protocol for Wireless Sensor Networks

FedPOIRec: Privacy-preserving federated poi recommendation with social influence

With the growing number of Location-Based Social Networks, privacy-preserving point-of-interest (POI) recommendation has become a critical challenge when helping users discover potentially interesting new places. Traditional systems take a centralized approach that requires the transmission and collection of private user data. In this work, we present FedPOIRec, a privacy-preserving federated learning approach enhanced with features from user social circles to generate top-N POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner’s device, while a parameter server blindly aggregates the local updates. Second, the local recommender results are personalized by allowing users to exchange their learned parameters, enabling knowledge transfer among friends. To this end, we propose a privacy-preserving protocol for integrating the preferences of the user’s friends, after the federated computation, by exploiting the properties of the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach to five real-world datasets using two recommendation models. Extensive experiments demonstrate that FedPOIRec achieves comparable recommendation quality to centralized approaches, while the social integration protocol incurs low computation and communication overhead on the user device.

Decentralized Identity Authentication with Auditability and Privacy

Decentralized identity (DID) systems aim to give users full control over their identities by using completely decentralized technologies, such as blockchain or distributed ledgers, as identity providers. However, when user credentials are compromised, it is impossible in existing DID systems for the users to detect credential misuse. In this paper, we propose new DID authentication protocols with two properties: auditability and privacy. The former enables the detection of malicious authentication events, while the latter prevents an adversary from linking an authentication event to the corresponding user and service provider. We present two protocols that achieve auditability with varying privacy and performance guarantees. The first protocol has high performance, but it reveals information about the user. The second protocol achieves full privacy, but it incurs a higher performance overhead. We present a formal security analysis of our privacy-preserving protocols by using the Tamarin prover. We implemented them and evaluated their performance with a permissioned blockchain deployed over the Amazon AWS and a local cloud infrastructure. The results demonstrate that the first protocol is able to support realistic authentication workloads, while the second is nearly practical.

Privacy-preserving Collaborative Filtering by Distributed Mediation

Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely exclusively on training data consisting of past transactions that took place through them, it would be beneficial to base recommendations on the rating data of more than one vendor. However, enlarging the training data by means of sharing information between different vendors may jeopardize the privacy of users. We devise here secure multi-party protocols that enable the practice of Collaborative Filtering (CF) in a manner that preserves the privacy of the vendors and users. Shmueli and Tassa [ 38 ] introduced privacy-preserving protocols of CF that involved a mediator; namely, an external entity that assists in performing the computations. They demonstrated the significant advantages of mediation in that context. We take here the mediation approach into the next level by using several independent mediators. Such distributed mediation maintains all of the advantages that were identified by Shmueli and Tassa, and offers additional ones, in comparison with the single-mediator protocols: stronger security and dramatically shorter runtimes. In addition, while all prior art assumed limited and unrealistic settings, in which each user can purchase any given item through only one vendor, we consider here a general and more realistic setting, which encompasses all previously considered settings, where users can choose between different competing vendors. We demonstrate the appealing performance of our protocols through extensive experimentation.

Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities

Data sensing and gathering is an essential task for various information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffer from limited sensing coverage. On the other hand, data can also be gathered dynamically through crowdsensing contributed by voluntary users but suffer from its unreliability and the lack of incentives for users’ contributions. In this article, we explore an integrated paradigm called “ hybrid sensing ” that harnesses both IoT-sensing and crowdsensing in a complementary manner. In hybrid sensing, users are incentivized to provide sensing data not covered by IoT sensors and provide crowdsourced feedback to assist in calibrating IoT-sensing. Their contributions will be rewarded with credits that can be redeemed to retrieve synthesized information from the hybrid system. In this article, we develop a hybrid sensing system that supports explicit user privacy—IoT sensors are obscured physically to prevent capturing private user data, and users interact with a crowdsensing server via a privacy-preserving protocol to preserve their anonymity. A key application of our system is smart parking, by which users can inquire and find the available parking spaces in outdoor parking lots. We implemented our hybrid sensing system for smart parking and conducted extensive empirical evaluations. Finally, our hybrid sensing system can be potentially applied to other information-driven services in smart cities.

Cross-Layer Device Authentication With Quantum Encryption for 5G Enabled IIoT in Industry 4.0

Industrial Internet of Things (IIoT), a core enabler of Industry 4.0, is evolving rapidly to tackle the challenges imposed by explosive real-time manufacturing data in the context of Internet and telecommunication industry. 5G technology is the key to addressing such challenges. This is done by bypassing upper authentication protocols and supporting small data transmission during initial access, which, however, causes serious security breaches in IIoT device authentication. To solve this, in this article propose a secure cross-layer authentication framework based on quantum walk on circles. The system performs random hash coding on multidomain physical-layer resources to encode and decode device identifiers securely, while using a quantum walk based privacy-preserving protocol to maintain code privacy at arbitrary high level, being controlled by the number of occupied physical resources. The upper bound of decoding errors is derived and a nonconvex integer programming problem of minimizing the bound is formulated to characterize the security performance. The space of one-time keys for encryption is also derived that show how high privacy and scalability advantage is maintained against classical and quantum computers. Finally, we derive novel expressions of failure probability of this new authentication system and numerically show that our scheme can bring ultrahigh level of security and privacy protection with low latency despite attack.