Strong Privacy Preservation Research Articles

A Robust and Lightweight Privacy-Preserving Data Aggregation Scheme for Smart Grid

Privacy-preserving data aggregation (PPDA) enables data availability and privacy preservation simultaneously in smart grid. However, existing methods, such as masking and homomorphic encryption, cannot simultaneously offer strong privacy preservation, fault tolerance for both smart meters and aggregators, verifiable aggregation, and lightweight encryption. To tackle these challenges, we design HTV-PRE, a homomorphic threshold proxy re-encryption scheme with re-encryption verifiability. HTV-PRE involves only linear operations and resists quantum attacks after being instanced by ideal lattices. By leveraging HTV-PRE, we propose a robust and lightweight data aggregation scheme with strong privacy preservation for smart grid. Robustness ensures fault tolerance and error detection. Even if some smart meters or aggregators are faulty, data aggregation can still work without imposing expensive computation on other smart meters or requiring additional trust assumptions. Additionally, to detect aggregators' errors, a proof for the aggregated result is presented so that anyone can verify whether the result has been correctly computed or not. The verifiable aggregation adds no computation/communication overhead on the user side. The performance evaluations demonstrate that our PPDA scheme significantly offloads computation overhead from smart meters and control center to the edge, and its user encryption is up to 4x faster than existing approaches.

Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation

In the era of Big Data, deep learning techniques provide intelligent solutions for various problems in real-life scenarios. However, deep neural networks depend on large-scale datasets including sensitive data, which causes the potential risk of privacy leakage. In addition, various constantly evolving attack methods are also threatening the data security in deep learning models. Protecting data privacy effectively at a lower cost has become an urgent challenge. This paper proposes an Adaptive Feature Relevance Region Segmentation (AFRRS) mechanism to provide differential privacy preservation. The core idea is to divide the input features into different regions with different relevance according to the relevance between input features and the model output. Less noise is intentionally injected into the region with stronger relevance, and more noise is injected into the regions with weaker relevance. Furthermore, we perturb loss functions by injecting noise into the polynomial coefficients of the expansion of the objective function to protect the privacy of data labels. Theoretical analysis and experiments have shown that the proposed AFRRS mechanism can not only provide strong privacy preservation for the deep learning model, but also maintain the good utility of the model under a given moderate privacy budget compared with existing methods.

Efficient Strong Privacy-Preserving Conjunctive Keyword Search Over Encrypted Cloud Data

Searchable symmetric encryption (SSE) supports keyword search over outsourced symmetrically encrypted data. Dynamic searchable symmetric encryption (DSSE), a variant of SSE, further enables data updating. Most DSSE works with conjunctive keyword search primarily consider forward and backward privacy. Ideally, the server should only learn the result sets involving all keywords in the conjunction. However, existing schemes suffer from keyword pair result pattern (KPRP) leakage, revealing the partial result sets containing two of query keywords. We propose the first DSSE scheme to address aforementioned concerns that achieves strong privacy-preserving conjunctive keyword search. Specifically, our scheme can maintain forward and backward privacy and eliminate KPRP leakage, offering a higher level of security. The search complexity scales with the number of documents stored in the database in several existing schemes. However, the complexity of our scheme scales with the update frequency of the least frequent keyword in the conjunction, which is much smaller than the size of the entire database. Besides, we devise a least frequent keyword acquisition protocol to reduce frequent interactions between clients. Finally, we analyze the security of our scheme and evaluate its performance theoretically and experimentally. The results show that our scheme has strong privacy preservation and efficiency.

SmartDID: A Novel Privacy-Preserving Identity Based on Blockchain for IoT

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.

An Efficient Privacy-Enhancing Cross-Silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids

Federated Learning is a prominent machine learning paradigm which helps tackle data privacy issues by allowing clients to store their raw data locally and transfer only their local model parameters to an aggregator server to collaboratively train a shared global model. However, federated learning is vulnerable to inference attacks from dishonest aggregators who can infer information about clients’ training data from their model parameters. To deal with this issue, most of the proposed schemes in literature either require a non-colluded server setting, a trusted third-party to compute master secret keys or a secure multiparty computation protocol which is still inefficient over multiple iterations of computing an aggregation model. In this work, we propose an efficient cross-silo federated learning scheme with strong privacy preservation. By designing a double-layer encryption scheme which has no requirement to compute discrete logarithm, utilizing secret sharing only at the establishment phase and in the iterations when parties rejoin, and accelerating the computation performance via parallel computing, we achieve an efficient privacy-preserving federated learning protocol, which also allows clients to dropout and rejoin during the training process. The proposed scheme is demonstrated theoretically and empirically to provide provable privacy against an honest-but-curious aggregator server and simultaneously achieve desirable model utilities. The scheme is applied to false data injection attack detection (FDIA) in smart grids. This is a more secure cross-silo FDIA federated learning resilient to the local private data inference attacks than the existing works.

Differential privacy: Review of improving utility through cryptography‐based technologies

SummaryDue to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention. In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state‐of‐the‐art and to benefit future researches, we are motivated to provide this review.

Multi-UAV Cooperative Path Planning with Monitoring Privacy Preservation

UAVs have shown great potential application in persistent monitoring, but still have problems such as difficulty in ensuring monitoring frequency and easy leakage of monitoring path information. Therefore, under the premise of covering all monitoring targets by UAVs, it is necessary to improve the monitoring frequency of the target and the privacy protection of the monitoring intention as much as possible. In response to the above problems, this research proposes monitoring overdue time to evaluate the monitoring frequency and monitoring period entropy in order to evaluate the ability to ensure monitoring privacy protection. It then establishes a multi-UAV cooperative persistent monitoring path planning model. In addition, the multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is improved based on the monitoring overdue time. Finally, an optimal flight path for multi-UAV monitoring with high monitoring frequency and strong privacy preservation of monitoring intention is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node and the privacy preservation of the UAV monitoring path and has great significance for enhancing security monitoring and preventing intrusion.

Model compression and privacy preserving framework for federated learning

Federated learning (FL) as a collaborative learning paradigm has attracted extensive attention due to its characteristic of privacy preserving, in which the clients train a shared neural network model collaboratively by their local dataset and upload their model parameters merely instead of original data by wireless network in the whole training process. Because FL reduces transmission significantly, it can further meets the efficiency and security of the next generation wireless system. Although FL has reduced the size of information that needs to be transmitted, the update of model parameters still suffers from privacy leakage and communication bottleneck especially in wireless networks. To address the problem of privacy and communication, this paper proposes a model compression based FL framework. Firstly, the designed model compression framework provides effective support for efficient and secure model parameters updating in FL while keeping the personalization of all clients. Then, the proposed perturbed model compression method can further reduce the size of the model and protect the privacy of the model without sacrificing much accuracy. Besides, it also facilitates the simultaneous execution of decryption and decompressing operations by reconstruction algorithm on encrypted and compressed model parameters which is obtained by the proposed perturbed model compression method. Finally, the illustrative results demonstrate that the proposed model compression based FL framework can significantly reduce the number of model parameters for uploading with a strong privacy preservation property. For example, when the compression ratio is 0.0953 (i.e., only 9.53% of the parameters are uploaded), the accuracy of MNIST achieves 97% while the accuracy without compression is 98%.

An ensemble of random decision trees with local differential privacy in edge computing

Edge computing is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, the new paradigm is faced with security and privacy challenges due to the diversity and the limited capability of edge components. In particular, data privacy is one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in edge computing, which not only gives the strong privacy guarantee, but also provides a certain amount of data utility. Firstly, we design a preservation framework to implement private random decision trees satisfying local differential privacy. Secondly, we present the concrete implementations of algorithms and the corresponding task that each participant needs to undertake. Thirdly, we analyze the key factors to influence privacy and utility, including the allocation of data and privacy budget. Fourthly, we give the improved algorithms to further increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework.

Privacy-preserving AI for cybersecurity: Balancing threat intelligence collection with user data protection

This paper explores the case of using privacy-preserving artificial intelligence in cybersecurity by analyzing the importance of effective threat intelligence in the conflict with potential invasions and high user data protection standards. With the increased articulation of cyber threats, AI is crucial in fortifying detection, reaction, and prevention measures for cyber threats in CSFs. However, such large-scale information feeding these systems raises many privacy issues, and hence, strong privacy preservation mechanisms that ensure user anonymity and protect the information from misuse are needed. This study reveals how AI threat detection accuracy can be preserved while protecting users' privacy through data obfuscation, differential privacy, and federated learning. Furthermore, the article highlights the need to apply privacy-enhancing patterns, including Privacy by Design, as new patterns in cybersecurity lifecycles. The recommendations derived here are intended to help researchers and practitioners achieve equal data protection results and threat intelligence efficiency when employing AI models. This approach promotes a secure and highly sensitive terrain for disseminating AI-assisted cybersecurity innovations.

An efficient authentication scheme with strong privacy preservation for fog-assisted vehicular ad hoc networks based on blockchain and neuro-fuzzy

Privacy, security and efficiency are important performance issues in vehicular ad hoc network (VANET), which researchers have tried to address in recent years. This research article proposes a lightweight and privacy-preserving certificateless authentication scheme in fog-assisted VANET using blockchain technology and neuro-fuzzy machine learning technique. A new authentication scheme is designed using certificateless signature based on elliptic curve cryptography (ECC) and hash function operation. The scheme utilizes two blockchains to achieve decentralized and transparent transactions and revocation process. In order to prevent denial-of-service attack, in which a roadside unit (RSU) is flooded with a massive amount of fake authentication requests so as to prevent legitimate nodes from being authenticated, a neuro-fuzzy algorithm is implemented to proactively detect and discard any anomalous requests prior to an authentication process. Moreover, the scheme is demonstrated to be semantically secure in the random oracle model (ROM) based on the intractability of the discrete logarithm problem (DLP). A panoptic analysis shows that the scheme possesses outstanding attributes required for a secure vehicular communication system. The experimental simulation is conducted using simulation of urban mobility (SUMO) and the broadly-accepted network simulator NS-3. The results indicate that the proposed scheme has a high efficiency in terms of transmission delay and message delivery rate. The comparative analysis with the state-of-the-art schemes reveals that the proposed scheme has an improvement of 50%–90.5% in computation cost and 38.46%–69.6% in communication overhead. The simulation result of the neuro-fuzzy gives an accuracy of 91.5% and further analysis shows that it significantly reduces the computation burden on the RSU proportionately with increase in the number of malicious messages.

A Survey on Privacy-Preserving Authentication Schemes in VANETs: Attacks, Challenges and Open Issues

Vehicular ad hoc Networks (VANETs) are an emerging technology with robust applications in Intelligent Transport System. It consists of smart vehicles and roadside infrastructure which communicate through open access wireless networks. The rapid growth in vehicles results in VANETs becoming large-scale, dynamic, heterogeneous and it is possible for the attacker to harm vehicular communication which leads to life-endangering situations. VANETs must ensure secured vehicular communication using strong privacy-preserving and authentication mechanisms. In addition, efficiency is also a major concern in VANETs. Numerous studies have been discussed in literature for VANETs privacy and security. Nevertheless, no one covered the privacy and security issues as a holistic view. In this paper, we have given a detailed background overview of VANETs. Details of different possible attacks in VANET are also given in this paper. We have classified privacy and authentication schemes into four major groups with their security mechanisms, security requirements, strength, limitations, attacks countermeasures and performance measures. Finally, we have discussed some open issues in the field of VANETs security.

Applications of Differential Privacy in Social Network Analysis: A Survey

Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.

Deep Robust Cramer Shoup Delay Optimized Fully Homomorphic For IIOT secured transmission in cloud computing

Several sensors obtain data during industrial outturn and send this collected data to the cloud server via Internet communication. Due to the reason that a cloud server is not an entirely trusted entity, data authenticity has to be provided prior to outsource to the cloud server, so that only authorized users or devices can access those authentic data from distinct topography areas. Hence, a strong privacy preservation mechanism is required during data collection. In addition, the data latency and network delay involved in communication should also be observed. In order to robust privacy preservation, Robust Cramer Shoup Delay Optimized Fully Homomorphic (RCS-DOFH) is proposed. This method includes three steps. First to minimize the communication overhead and time, Kullback–Leibler divergence is used in the Robust Cramer Shoup Decryption (RCSD) mechanism. Next, to minimize the data latency and network delay, Delay Optimized Fully Homomorphic Encryption (DOFHE) mechanism is designed. In this mechanism, delivery delay is calculated between the base station and IIoT device signal Finally, privacy preserving deep learning using RCSD and DOFHE is presented for privacy preserved secure data transmission. At first, RCSD mechanism is utilized to decrypt private generated signals along with its weight parameters. Then, the encryption is performed by using DOFHE mechanism. After that, activation function over the encryption region is determined. By this way, the proposed RCS-DOFH method achieves secure data transmission with minimum latency and network delay. The comparison of the RCS-DOFH method is provided and experiments conducted on SECOM dataset showed that the proposed method outperforms other conventional methods.

Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for special interest customers. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation from geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency on computation and communication.

Highly efficient federated learning with strong privacy preservation in cloud computing

Federated learning is a new machine learning framework that allows mutually distrusting clients to reap the benefits from the joint training model without explicitly disclosing their private datasets. However, the high communication cost between the cloud server and clients has become the main challenge due to the limited network bandwidth. Moreover, the model parameters it shares may be utilized to perform model inversion attacks. Aimed at these problems, a new scheme for highly efficient federated learning with strong privacy preservation in cloud computing is presented. We design a lightweight encryption protocol to provide provably privacy preservation while maintaining desirable model utility. Additionally, an efficient optimization strategy is employed to enhance the training efficiency. Under the defined threat model, we prove the proposed scheme is secure against the honest-but-curious server and extreme collusion. We evaluate the effectiveness of our scheme and compare it with existing related works on MNIST and UCI Human Activity Recognition Dataset. Results show that our scheme reduces the execution time by 20% and transmitted ciphertext size by 85% on average while achieving similar accuracy as the compared secure multiparty computation (SMC) based methods.

Efficient Privacy Preserving Data Collection and Computation Offloading for Fog-Assisted IoT

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

Lightweight Privacy Preservation for Securing Large-Scale Database-Driven Cognitive Radio Networks with Location Verification

The database-driven cognitive radio networks (CRNs) are regarded as a promising approach to utilizing limited spectrum resources in large-scale Internet of Things (IoT). However, database-driven CRNs face some security and privacy threats. Firstly, secondary users (SUs) should send identity and location information to the database (DB) to obtain a list of available channels, such that the curious DB might easily misuse and threaten the privacy of SUs. Secondly, malicious SUs might send fake location information to the DB in order to occupy channels with better quantity in advance and so gain benefits. This might also cause serious interference to primary users (PUs). In this paper, we propose a lightweight privacy-preserving location verification protocol to protect the identity and location privacy of each SU and to verify the location of SUs. In the proposed protocol, the SU does not need to provide location information to request an available channel from the DB. Therefore, the DB cannot get the location information of any SU. In the proposed protocol, the base station (BS) selects some SUs as witnesses to generate location proofs for each other in a distributed fashion. This new witness selection mechanism makes the proposed protocol reliable when a malicious SU generates fake location information to cheat the BS and also prevents SU-Witness collusion attacks. The results also show that the proposed protocol can provide strong privacy preservation for SUs and can effectively verify the location of the SUs. The security analysis shows that the proposed protocol can resist various types of attacks. Moreover, compared with previous protocols, the proposed protocol is lightweight because it relies on symmetric cryptography and it is unaffected by the area covered by the DB.

An efficient privacy-preserving protocol for database-driven cognitive radio networks

Cognitive radio is regarded as a promising technology for remedying spectrum shortage and improving spectrum utilization. However, database-driven cognitive radio networks (CRNs) are vulnerable to a series of security attacks and privacy threats, especially the privacy breach of secondary users (SUs). In this paper, we propose an efficient privacy-preserving protocol to protect the identity and trajectory privacy of each SU and to prevent the database (DB) from tracing any SU. Besides, this paper also aims at improving the efficiency of authentication and channel allocation to offer the best connectivity to SUs wherever they are and at any time. Taking advantage of the elliptic curve cryptography technique, our protocol achieves the mutual authentication between SUs and base stations (BSs), which enhances the security of CRNs communication. Based on channel pre-allocation to BSs, our protocol reduces the latency of obtaining available channels for SUs. Moreover, our protocol exploits reputation to motivate SUs to be honest and thus increases the utilization of the limited spectrum resources. The results of security analysis show that our protocol provides strong privacy preservation for SUs that the DB cannot get the entire trajectory of any SU, even the DB colludes with all BSs. Furthermore, the results also show that the proposed protocol can resist various types of attacks. The results of performance evaluation indicate the efficiency of the proposed privacy-preserving protocol.

A real-time aggregate data publishing scheme with adaptive <i>ω</i>-event differential privacy

Although massive real-time data collected from users can provide benefits to improve the quality of human daily lives, it is possible to expose users' privacy. $\epsilon$-differential privacy is a notable model to provide strong privacy preserving in statistics. The existing works highlight $ω$-event differential privacy with a fixed window size, which may not be suitable for many practical scenarios. In view of this issue, we explore a real-time scheme with adaptive $ω$-event for differentially private time-series publishing (ADP) in this paper. In specific, we define a novel notion, Quality of Privacy (QoP) to measure both the utility of the released statistics and the performance of privacy preserving. According to this, we present an adaptive $ω$-event differential privacy model that can provide privacy protection with higher accuracy and better privacy protection effect. In addition, we also design a smart grouping mechanism to improve the grouping performance, and then improve the availability of publishing statistics. Finally, comparing with the existing schemes, we exploit real-world and synthetic datasets to conduct several experiments to demonstrate the superior performance of the ADP scheme.