Privacy-preserving Algorithm Research Articles

K-anonymity privacy-preserving algorithm for IoT applications in virtualization and edge computing

K-anonymity privacy-preserving algorithm for IoT applications in virtualization and edge computing

Friendship links-based privacy-preserving algorithm against inference attacks

Directly publishing the original data of social networks may compromise personal privacy because social relationship data contain sensitive information about users. To protect the social relationships against inference attacks and achieve the trade-off between privacy and utility, we propose a privacy protection algorithm that combines the friendship links of central nodes (PPCN) in a dynamic social network. In the preparation work, we design two indices for user influence based on the characteristics of social networks that can identify central nodes (Definition 1) in a network. Operating central nodes can effectively protect user privacy and improve algorithm efficiency. Then we propose the PPCN algorithm to classify the friendship links of central nodes into three levels, which achieves the trade-off between privacy and utility. Considering that the added links may increase the risk of privacy disclosure, a substitution coefficient θ (Definition 4) is designed to measure the probability of two strangers becoming friends. Experimental results show that the privacy-utility trade-off (PUTO) value of the PPCN algorithm is an average 29.43% lower than that of other methods, achieving a better trade-off between privacy and structural utility. In addition, the PPCN algorithm only runs for 3.59 s, which performs better than most algorithms.

Privacy-Preserving Feature Selection with Fully Homomorphic Encryption

For the feature selection problem, we propose an efficient privacy-preserving algorithm. Let D, F, and C be data, feature, and class sets, respectively, where the feature value x(Fi) and the class label x(C) are given for each x∈D and Fi∈F. For a triple (D,F,C), the feature selection problem is to find a consistent and minimal subset F′⊆F, where ‘consistent’ means that, for any x,y∈D, x(C)=y(C) if x(Fi)=y(Fi) for Fi∈F′, and ‘minimal’ means that any proper subset of F′ is no longer consistent. On distributed datasets, we consider feature selection as a privacy-preserving problem: assume that semi-honest parties A and B have their own personal DA and DB. The goal is to solve the feature selection problem for DA∪DB without sacrificing their privacy. In this paper, we propose a secure and efficient algorithm based on fully homomorphic encryption, and we implement our algorithm to show its effectiveness for various practical data. The proposed algorithm is the first one that can directly simulate the CWC (Combination of Weakest Components) algorithm on ciphertext, which is one of the best performers for the feature selection problem on the plaintext.

Efficient Privacy-Preserving Red Deer Optimization Algorithm with Blockchain Technology for Clustered VANET

Efficient Privacy-Preserving Red Deer Optimization Algorithm with Blockchain Technology for Clustered VANET

DESIGN AND DEVELOPMENT OF ANTI-LOCK BRAKING SYSTEM FOR ELECTRIC VEHICLE

Abstract—Recently, privacy-preserving association rules mining algorithms have been proposed to support data privacy. However, the algorithms have an additional overhead to insert fake items (or fake transactions) and cannot hide data frequency. In this paper, we propose a privacy-preserving association rule mining algorithm for encrypted data in cloud computing. For association rule mining, we utilize Apriori algorithm by using the Elgamal cryptosystem, without additional fake transactions. Thus the proposed algorithm can guarantee both data privacy and query privacy, while concealing data frequency. We show that the proposed algorithm achieves about 3-5 times better performance than the existing algorithm, in terms of association rule mining time. Keywords- association rule mining; Apriori algorithm; encrypted data; cloud computing; Elgamal cryptosystem;

Toward Vehicular Digital Forensics From Decentralized Trust: An Accountable, Privacy-Preserving, and Secure Realization

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy preservation while providing secure data access control in VDF is a nontrivial challenge. To mitigate this issue, in this article, we propose a blockchain-based decentralized solution for VDF named BB-VDF, in which the accountable protocols and privacy-preserving algorithm are constructed. The desirable security properties and fine-grained data access control are achieved based on smart contract and the customized cryptographic construction. Specifically, we design a distributed key-policy attribute-based encryption scheme with partially hidden access structures, named DKP-ABE-H, to realize the secure fine-grained forensics data access control. Further, a novel smart contract is designed to model the forensics procedures as a finite state machine, which guarantees accountability that each participant performs auditable cooperation under tamper resistant and traceable transactions. Systematic security analysis and extensive experimental results show the feasibility and practicability of our proposed BB-VDF scheme.

A consensus-based privacy-preserving energy management strategy for microgrids with event-triggered scheme

In terms of the distributed energy management (DEM) in microgrids, increasing distributed generators and users will bring tremendous data transmitted in the communication network. Considering the data security and the computation efficiency, protecting the participants’ privacy and saving the communication network resources are essential. This paper proposes a consensus-based distributed privacy-preserving energy management strategy with the event-triggered scheme to achieve privacy-preserving and network resource-saving simultaneously. Firstly, the DEM in microgrids is formulated as a social welfare maximization problem. Then the privacy disclosure of both generators and consumers under the consensus-based distributed control strategy is analyzed. Next, a consensus-based privacy-preserving algorithm using differential privacy is designed to protect private information. An event-triggered scheme is applied to the strategy to save network resources. Besides, theoretical analysis, including convergence, accuracy and differential privacy, is presented to guarantee the algorithm’s sufficiency. The trade-off between the accuracy and privacy level is also investigated. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.

Privacy-preserving genotype imputation with fully homomorphic encryption.

Genotype imputation is the inference of unknown genotypes using known population structure observed in large genomic datasets; it can further our understanding of phenotype-genotype relationships and is useful for QTL mapping and GWASs. However, the compute-intensive nature of genotype imputation can overwhelm local servers for computation and storage. Hence, many researchers are moving toward using cloud services, raising privacy concerns. We address these concerns by developing an efficient, privacy-preserving algorithm called p-Impute. Our method uses homomorphic encryption, allowing calculations on ciphertext, thereby avoiding the decryption of private genotypes in the cloud. It is similar to k-nearest neighbor approaches, inferring missing genotypes in a genomic block based on the SNP genotypes of genetically related individuals in the same block. Our results demonstrate accuracy in agreement with the state-of-the-art plaintext solutions. Moreover, p-Impute is scalable to real-world applications as its memory and time requirements increase linearly with the increasing number of samples. p-Impute is freely available for download here: https://doi.org/10.5281/zenodo.5542001.

A Secure and Efficient Energy Trading Model Using Blockchain for a 5G-Deployed Smart Community

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs’ charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain-based privacy-preserving energy trading system for 5G-deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation-based distributed matching algorithm for EVs and a Reward-based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short-term load forecasting model for EVs’ charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity-based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs’ component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers’ component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs’ charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy-preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug-free against the common vulnerabilities’ attacks. The results show that the smart contracts are secure against both internal and external attacks.

An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes

Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual systems within CRNs often cannot share patient-level data due to privacy regulations, prohibiting multi-site regression which requires an analyst to access all individual patient data pooled together. Meta-analysis is commonly used to model data stored at multiple institutions within a CRN but can result in biased estimation, most notably in rare-event contexts. We present a communication-efficient, privacy-preserving algorithm for modeling multi-site zero-inflated count outcomes within a CRN. Our method, a one-shot distributed algorithm for performing hurdle regression (ODAH), models zero-inflated count data stored in multiple sites without sharing patient-level data across sites, resulting in estimates closely approximating those that would be obtained in a pooled patient-level data analysis. We evaluate our method through extensive simulations and two real-world data applications using electronic health records: examining risk factors associated with pediatric avoidable hospitalization and modeling serious adverse event frequency associated with a colorectal cancer therapy. In simulations, ODAH produced bias less than 0.1% across all settings explored while meta-analysis estimates exhibited bias up to 12.7%, with meta-analysis performing worst in settings with high zero-inflation or low event rates. Across both applied analyses, ODAH estimates had less than 10% bias for 18 of 20 coefficients estimated, while meta-analysis estimates exhibited substantially higher bias. Relative to existing methods for distributed data analysis, ODAH offers a highly accurate, computationally efficient method for modeling multi-site zero-inflated count data.

High-performance implementation of evolutionary privacy-preserving algorithm for big data using GPU platform

Privacy protection has become a predominant concern in big data analysis. Privacy-Preserving Association Rule Mining (PPARM) is a process in which original data is transformed into a sanitized version to remove sensitive patterns. Although evolutionary PPARM approaches were developed, they are inefficient because the fitness function is overwhelmingly expensive. In this paper, an efficient algorithm, GPU-based Evolutionary Privacy-Preserving (GEPP), for improving the performance of PPARM is presented. We introduce two strategies for this reason: the first one is to develop a parallel indexing machine to generate retrieval index lists by parallelizing dataset scanning between GPU blocks, and the second strategy is the parallelization of the index lists for fitness function computation using CPU/GPU platform. In the second strategy, search mechanism and fitness function steps are performed on CPU and GPU, respectively. The scalability of GEPP is evaluated in terms of GPU characteristics, e.g., blocks and threads, data characteristics, e.g., the number of transactions, items, and density ratio, and patterns characteristics, e.g., the ratio of sensitive patterns and support thresholds. Experimental results show that our parallel implementation of the proposed approach obtains a speedup up to 36.6x, on average, compared to the CPU implementation using real and synthetic large-scale transaction datasets.

Privacy-Preserving Consensus for Multi-Agent Systems via Node Decomposition Strategy

This paper proposes two kinds of algorithms to achieve privacy-preserving consensus of multi-agent systems over undirected graphs via node decomposition mechanism and homomorphic cryptography technique. Based on the number of neighboring nodes ( |\mathscr N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> |), every agent is decomposed into |\mathscr N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> | subagents, which are connected as a chain graph. Note that every subagent connects one and only one non-homologous subagent (generated by different agents). Information interaction between non-homologous subagents is encrypted by a homomorphic cryptography algorithm, and homologous subagents exchange information directly. In this regard, the proposed node decomposition mechanism enhances the privacy of the initial values without increasing the computational complexity of encryption. The first privacy-preserving algorithm can achieve the accurate average consensus, which means that the agreement value of every subagent is consistent with the original average consensus value. The second algorithm studies the privacy-preserving scaled consensus problem without a priori knowledge about the underlying graph. Although the final convergence values of subagents do not keep exactly the same, homologous subagents can compute the original group decision value by resorting to the product of the limit value and agent's degree. Importantly, this algorithm also guarantees the privacy of group decision value of the whole system. Besides, it is proved that the privacy of the initial value can be preserved if the agent has at least one neutral neighbor.

Privacy Preserving Algorithm using Chao-Scattering of Partial Homomorphic Encryption

A big challenge in privacy-preserving is found when trying is to share data while protecting the information of personally identifiable, such as phone numbers, email addresses, and biometric. The same challenges have appeared when keeping sensitive data remotely in complete privacy, without recognizing them from any unauthorized persons. In such cases, Homomorphic encryption is the highlight and considered the most suitable solution. The goal of this paper is to keep the privacy of the data stored in a database system using a new Chao-modification to the partial homomorphic encryption algorithm. It uses the chaotic system to scatter the symbols of plain data before direct them to the encryption algorithm. This will be appending an additional level of security to partial homomorphic encryption algorithms that helps them to be more resisting attacking.

Privacy-Preserving Algorithm for Decoupling of Multi-Agent Plans with Uncertainty

The execution of multi-agent plans often requires communication between agents in order to synchronize their tasks. In cases where communication is unreliable or undesirable, temporal decoupling algorithms allow agents to find a distributed execution strategy beforehand without requiring perfect communication on the fly. The state-of-the-art Multi-Agent Simple Temporal Network with Uncertainty (MaSTNU) framework extends the decoupling problem for Multi-Agent Simple Temporal Network (MaSTN) to allow the modeling of uncertain durations and allow agents to communicate when certain events occur and communication is available. However, the existing approach assumes centralized knowledge of the MaSTNU, whereas in the multi-agent context, privacy is an important concern. In this paper, we propose a distributed, privacy-preserving algorithm for finding distributed execution strategies for MaSTNU. Experiments also showed significant speed-up of the proposed algorithm when the multi-agent plan is loosely coupled and mostly private.

Spatiotemporal Mobility Based Trajectory Privacy-Preserving Algorithm in Location-Based Services.

Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user’s trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user’s trajectory privacy. However, the user’s attributes are rarely considered when constructing the k-anonymity set. It results in that the user’s trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user’s attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user’s attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.

A Random-Weight Privacy-Preserving Algorithm With Error Compensation for Microgrid Distributed Energy Management

Recently, collaborative distributed energy management systems (CoDEMS) have emerged as an effective solution to manage distributed energy resources in microgrid. In CoDEMS, devices collaborate in a distributive manner over communication networks to meet electrical loads and supply balance at minimum cost. However, mutual information exchanges among the devices in CoDEMS may leak important information about the devices states. In this paper, we investigate the challenging problem of how to achieve optimality while preserving the privacy of CoDEMS at relatively low cost. Unlike many previous works that preserve the privacy by using additive noises, we propose a novel random-weight privacy-preserving algorithm with error compensation, termed as REP-CoDEMS, for CoDEMS. In the proposal, each distributed device generates two random weights each time and it communicates with its neighbor conveying values based on the weights, incremental cost estimation and power imbalance estimation information along with a novel error compensation term to eliminate the error induced by the random weights. We theoretically prove that the proposed REP-CoDEMS algorithm converges and preserves the privacy of all devices. We also derive analytical expressions of the maximum privacy disclosure probability for initial and final states of the CoDEMS. In addition, we conduct extensive simulations and the results demonstrate the effectiveness of the proposed algorithm.

Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid

In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency conditions. However, due to the large problem size and real-time requirement, a fast and robust technique is required to solve this problem. Moreover, due to privacy concerns, it is important that the data remains confidential and processed on local computers. Therefore, a fully privacy-preserving algorithm is proposed which performs computation directly over the encrypted SCOPF problem. The SCOPF is decomposed into smaller subproblems corresponding to individual pre-contingency and post-contingency cases using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. Both algorithms are presented for solving the SCOPF problem in a privacy-preserving and distributed manner. Security analysis shows that our algorithm can preserve both system confidentiality and data privacy. Performance evaluations validate the correctness and effectiveness of the proposed algorithm.

Consensus with preserved privacy against neighbor collusion

This paper proposes a privacy-preserving algorithm to solve the average-consensus problem based on Shamir’s secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual states until an agreement is reached. Unlike other methods, the proposed algorithm renders the network resistant to the collusion of any given number of neighbors (even with all neighbors’ colluding). Another virtue of this work is that such a method can protect the network consensus procedure from eavesdropping.

Latent Dirichlet Allocation Model Training With Differential Privacy

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various applications. However, the LDA model as well as the training process of LDA may expose the text information in the training data, thus bringing significant privacy concerns. To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios. In particular, we present the first theoretical analysis on the inherent differential privacy guarantee of CGS based LDA training and further propose a centralized privacy-preserving algorithm (HDP-LDA) that can prevent data inference from the intermediate statistics in the CGS training. Also, we propose a locally private LDA training algorithm (LP-LDA) on crowdsourced data to provide local differential privacy for individual data contributors. Furthermore, we extend LP-LDA to an online version as OLP-LDA to achieve LDA training on locally private mini-batches in a streaming setting. Extensive analysis and experiment results validate both the effectiveness and efficiency of our proposed privacy-preserving LDA training algorithms.

Preserving Privacy in WiFi Localization With Plausible Dummy Locations

Benefiting from the development of wireless communication, WiFi localization plays a vital role in various mobile applications. However, users’ locations are breached by untrusted localization servers, thus disclosing users’ location privacy and other sensitive personal information. Existing works on WiFi localization preservation employed homomorphic encryption, which incurs extremely high overheads. On the other hand, lightweight methods like $k$ -anonymity and dummy-based approaches are hardly used for privacy preservation in WiFi localization scenarios due to their inherent drawbacks, i.e., $k$ -anonymity approaches rely on trusted third parties while dummy-based approaches are susceptible to spatio-temporal correlation attacks. To design an effective yet lightweight WiFi localization privacy algorithm, this paper proposes to reinforce dummy techniques with plausible dummy location to resist the attacks. This study presents a novel algorithm, referred to as Location Preservation Algorithm with Plausible Dummies (LPPD), which is the first attempt towards WiFi localization privacy preservation using dummy techniques. The benefits of the algorithm are three-fold: First, it is lightweight. Second, it does not rely on trusted third parties. Third, it can resist spatio-temporal correlation attacks. Moreover, this study implements a WiFi localization system that integrates the proposed privacy-preserving algorithm with a WiFi fingerprint based indoor localization algorithm. Experiments results are presented to demonstrate the effectiveness and efficiency of our approach when compared with existing algorithms.