Privacy Protection Method Research Articles

Bivariate Correlation-Based Attack: A Challenge for Privacy Preservation of Location Sequence Data

Abstract. Data correlation is a critical issue for location sequence data privacy protection. Series-indistinguishability provides a theoretical basis for the differential privacy protection of temporally correlated location data and is implemented by the correlated Laplace mechanism (CLM), which has become a novel privacy protection method. However, location sequence data is essentially a multivariate time series with data correlation not only within each dimension but also between different dimensions. The CLM-based location privacy scheme only adapts to the auto-correlation in each dimension and ignores cross-correlation between different dimensions, which compromises its privacy performance. To evaluate its actual privacy protection performance, we propose a bivariate correlation-based attack (BCA) utilizing a filtering method and theoretically derive the optimal filter parameters. Based on simulations and real data experiments, the results show that the privacy performance of the CLM-based privacy scheme is significantly reduced under BCA, confirming that this scheme cannot achieve complete series-indistinguishability for bivariate sequences. Furthermore, the results suggest that BCA is an effective tool for privacy performance evaluation.

Deep deterministic policy gradient caching method for privacy protection in Internet of Vehicles

To address the problem of low cache hit ratio in edge nodes for privacy-preserving in the Internet of Vehicles (IoV), a deep deterministic policy gradient caching (DDPGC) method was proposed. First, a taxi certified by a trusted authority acted as a second-level caching edge node to acquire hotspot data and store it in the local cache. It then broadcasted this information to the neighboring service requesting vehicles (SRVs). SRVs cached the broadcasted data locally and search for service requests in the order of priority of local cache, taxi, and cloud server when such requests arise. Secondly, a neural network was deployed in taxis and SRVs to maximize the caching benefit through deep reinforcement learning for decision replacement of their cached data. Finally, when SRVs were located in vehicle sparsity and could not obtain request data from neighboring vehicles, a combination of k-anonymity and random response perturbation mechanisms generated anonymity sets to send requests to cloud servers in an anonymous manner to obtain services while protecting user location privacy. Simulation experimental results show that DDPGC can effectively improve the vehicle cache hit ratio, reduce the frequency of SRV interaction with the cloud server, and effectively protect user privacy security.

Protecting Infinite Data Streams from Wearable Devices with Local Differential Privacy Techniques

The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting and securely sharing wearable device data has become a critical concern. This paper proposes a local differential privacy-preserving algorithm designed for continuous data streams generated by wearable devices. Initially, the data stream is sampled at key points to avoid prematurely exhausting the privacy budget. Then, an adaptive allocation of the privacy budget at these points enhances privacy protection for sensitive data. Additionally, the optimized square wave (SW) mechanism introduces perturbations to the sampled points. Afterward, the Kalman filter algorithm is applied to maintain data flow patterns and reduce prediction errors. Experimental validation using two real datasets demonstrates that, under comparable conditions, this approach provides higher data availability than existing privacy protection methods for continuous data streams.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

PQSF: post-quantum secure privacy-preserving federated learning

In federated learning, secret sharing is a key technology to maintain the privacy of participants’ local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data security of participants in the post-quantum era. In addition, existing privacy protection methods have the problem of high communication and computational overhead. Although the multi-stage secret sharing scheme proposed by Pilaram et al. is one of the effective solutions to the above problems, existing studies have proven the privacy leakage risk of this scheme. This paper firstly designs a new lattice-based multi-stage secret sharing scheme Improved-Pilaram to solve the security problem, which allows participants to use public vectors to reconstruct different secret values without changing the secret sharing. Based on Improved-Pilaram, this article proposes a post-quantum secure federated learning scheme PQSF. PQSF uses double masking technology to encrypt model parameters and achieves mask reconstruction through secret sharing. Since Improved-Pilaram is multi-stage, participants do not need to update their local secret shares frequently during training. Analysis and experimental results show that the PQSF proposed in this paper reduces the communication complexity between participants and reduces the computational overhead by about 20% compared with existing solutions.

Assessing the Recreational Fishers and their Catches based on Social Media Platforms: Privacy and Ethical Data Analysis Considerations

Social Media (SM) can offer insights to track recreational Fishing (Rf); Many Limitations Hinder Its Practical Application. The proportion and characteristics of RFs that share their catches remain unidentified. The objective was to enhance the surveillance abilities of RF by utilizing SM information. This study focuses on the sharing economy information transmission, network optimization of SM platforms, and privacy protection issues during data transmission. The study starts with the data transmission characteristics in SM platforms, analyzes the data ethics in SM information transmission from the perspectives of natural and economic data attributes, and then focuses on the privacy protection principles in network information transmission. Then, a privacy protection scheme based on a locally sensitive hash algorithm is constructed, and finally, an information transmission scheme and method optimization based on a network optimization module are proposed. The research gathered data using physical (face-to-face) surveys and digital (email) surveys to define marine RF that post catches on online platforms (“sharers”), together with additional demographic and fishing data. A comparative analysis was conducted on the computational convergence and accuracy of different privacy protection methods, and the optimal rule framework for data privacy protection was discussed. The observation results show that the efficiency of information transmission using the Minhash-SSNR method is slightly inferior to that using the OSNR-SSNR method. Minhash technology is based on a similarity comparison of dimensionality-reduced data. This leads to data causing the initially highly similar two sets of lists to be misjudged as not having enough similarity, thereby reducing some potential information dissemination opportunities and affecting the efficacy of information transmission. It can be seen that the Minhash-SSNR strategy can effectively send information to nodes with high similarity, preventing excessive information duplication within the system. Although the Minhash-SSNR strategy has a certain degree of decline in information transmission efficiency, it accounts for only about 5% compared to the OSNR-SSNR strategy, ensuring the essential operational stability of SM opportunity networks without significant impact. With few learning and training times, the network information transmission optimization module proposed in this study quickly achieved a lower exponential error. As the number of training sessions gradually increases, the exponential error of the network information transmission optimization module in this study is small, and the prediction accuracy of the method is high. RFs who captured a prize, iconic, or symbolic species tended to discuss their catches more. This research signifies significant progress in incorporating SM information to oversee RFs.

P-Chain: Towards privacy-aware smart contract using SMPC

Smart contract, as the representative application of blockchain, has recently fueled extensive research interests from both academia and industry. However, with its wide applications, the weaknesses of smart contract have been gradually revealed. The major barrier to the widespread adoption of smart contract involves concerns about on-chain privacy which refers to the details of input/output privacy. To address privacy concerns, we propose in this paper, P-Chain, a privacy-aware framework for smart contracts of permissioned blockchain to protect sensitive data of users based on Secure Multi-party Computation (SMPC). Unlike existing work that suffer several key drawbacks, including introducing a third party who could get the details of the deal, and high overhead for on-chain and off-chain communication, as well as lacking a privacy protection for output data, we enhance the privacy protection for smart contracts system by adding a new secure multi-party computation layer in P-Chain. Through secure multi-party computing, sensitive inputs of smart contracts are divided into multiple sub-inputs and sent to computing participants for operation respectively, which ensures that each participant can only access part of the user’s information. A stochastic strategy based on (t;n) threshold secret sharing to select calculating parties is also been proposed, which makes it difficult for an attacker to aggregate t of n participants for launching a collusive attack. In addition, we propose the output privacy protection method that makes it possible to reach a consensus without the need to know the output. The extensive experimental evaluation and analysis demonstrate that our scheme enjoys the advantages of calculation correctness, input–output privacy as well as anti-collusion.

Trajectory-aware privacy-preserving method with local differential privacy in crowdsourcing

In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leakage of the workers. Local differential privacy (LDP) is currently the latest technique to protect data privacy. However, most of LDP-based schemes have limitations in providing good utility due to extensive noise in perturbing trajectories. In this work, to balance the privacy and utility, we propose a novel pattern-aware privacy protection method called trajectory-aware privacy-preserving with local differential privacy (TALDP). The key idea is that, rather than applying the same degree of perturbation to all location points, we employ adaptive privacy budget allocation, assigning varied privacy budgets to individual location points, thereby mitigating the perturbation’s impact and enhancing overall utility. Meanwhile, to ensure the privacy, we give the different perturbing points to different privacy budgets according to their important degree for the patterns of the trajectories. In particular, we use Karman filter method to select the important location points and decide their privacy budgets. We conduct extensive experiments on three real datasets. The results show that our approach improves the utility over many other current methods while still provide good the privacy protection.

Group Coding Location Privacy Protection Method Based on Differential Privacy in Crowdsensing

Group Coding Location Privacy Protection Method Based on Differential Privacy in Crowdsensing

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products

In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

Efficient and adaptive secure cross-domain recommendations

In recommendation systems, cross-domain recommendation (CDR) has emerged as an effective method for solving the problem of data sparsity and cold start. However, challenges such as data heterogeneity, data security, and recommendation accuracy are faced by existing CDR methods. An efficient and adaptive secure cross-domain recommendation (EAAS-CDR) model is proposed. First, the Automatic Encoder–Decoder (AutoED) model is proposed to address variations in user–item interaction quantity and item characteristics across various domains. The AutoED model enables adaptive and secure cross-domain recommendations by modeling user preferences across various domains, unifying vector dimensions, and avoiding the release of the rating matrix. An efficient cross-domain privacy protection method is proposed to resist data leakage. To ensure feature invariance for knowledge transfer, the inherent low perturbation characteristics of the Johnson–Lindenstrauss Transform mechanism are leveraged and combined with the output of our encoder. Finally, domain relevance measurement metrics and a domain fusion algorithm are proposed to use the source domain knowledge in the target domain and improve the recommendation accuracy of the model. Experimental results demonstrate that our approach is not only more efficient but also more secure than existing CDR approaches.

Privacy protection of communication networks using fully homomorphic encryption based on network slicing and attributes

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

Privacy-Protection Method for Blockchain Transactions Based on Lightweight Homomorphic Encryption

Privacy-Protection Method for Blockchain Transactions Based on Lightweight Homomorphic Encryption

Research on Privacy Issues in Smart Metering System: An Improved TCN-Based NILM Attack Method and Practical DRL-Based Rechargeable Battery Assisted Privacy Preserving Method

Smart meters, as a key component of Advanced Metering Infrastructure (AMI), collect fine-grained electricity consumption data for demand response in smart grids. While this data improves the grid’s accuracy, it also poses significant threats to users’ privacy. In this paper, we study the privacy issue in smart metering systems from both attacker and defender perspectives. First, we propose an improved Temporal Convolutional Network (TCN) based Non-Intrusive Load Monitoring (NILM) attack method, which infers electrical appliance usage from public load curves, addressing the gradient vanishing, gradient exploding, and other problems while improving attack accuracy. Second, we develop a rechargeable battery-assisted energy management system to hide load characteristics of electrical appliances by adding physical noise, thus resisting NILM attacks. To address the privacy-cost trade-off optimization problem, we propose a Practical Deep Reinforcement Learning-based Rechargeable Battery assist Privacy Preserving Method (PRoP) that learns optimal battery charging/discharging policies. We design a novel privacy measurement method and constraints to ensure the feasibility of system deployment and prove PRoP’s effectiveness in resisting NILM attacks. Comprehensive evaluations demonstrate that our improved TCN-based NILM method achieves an attack success rate of over 80% on various electrical appliances, improving attack performance (MAE, RMSE) by 20% compared to existing methods while reducing model training time. Moreover, our proposed PRoP achieves a better trade-off between privacy protection and electricity cost than existing battery-assisted methods, reducing costs by 5% and the attack success ratio to 36%, while increasing the MAE and RMAE obtained by NILM by 3 times. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Smart grid, which can support bidirectional information transmission, has a series of advantages, such as high efficiency and high stability. However, it also brings a significant threat to users’ electricity privacy. Although encryption-based privacy protection methods have been deployed on terminal devices of smart grids to prevent privacy leaks, this method can often only defend against intrusive attacks and has little effect on non-intrusive attacks. To this end, this paper studies the privacy issues caused by non-intrusive attacks. Specifically, to better study the protection method, we first investigate the attack mechanism and design an improved TCN-based Non-Intrusive Load Monitoring method. Then, we propose a Practical Reinforcement learning-based rechargeable battery-assisted Privacy preserving method (PRoP) to defend against this attack physically. The most practical contribution of this paper is that, compared with existing battery-assisted privacy protection methods, we do not blindly pursue algorithm performance but fully consider the practical factors of deployment, such as limiting battery capacity and constraining battery charging and discharging behavior. This can guide practitioners to better apply this technology in practice.

Privacy-preserving method for sensitive partitions of electricity consumption data based on hybrid differential privacy and k-anonymity

Abstract With the development of smart grid, traditional privacy protection methods are no longer sufficient to cope with the mining and analysing of users’ electricity consumption data, which can easily lead to the leakage of users’ privacy. In this paper, for the challenge of privacy protection of user electricity data in smart grid, we propose a privacy protection method for sensitive partition of electricity data based on hybrid differential privacy and k-anonymity technique, which identifies pattern-specific quasi-identifiers by random forest technique and divides the data into privacy-invasive and non-privacy-invasive partitions, and adopts the Laplacian noise addition (numerical quasi-identifiers) and data generalisation (sub-types of quasi-identifiers) techniques to achieve differential privacy protection for different partitions. Finally, it is experimentally verified that the method performs better in terms of information reduction accuracy and execution time, effectively striking a balance between protecting user privacy and maintaining data utility.

Trajectory privacy protection method based on sensitive semantic location replacement

As Location-Based Services (LBSs) proliferate within the Social Internet of Vehicles (SIoVs), the collection and utilization of vehicle trajectories, which are rich in spatio-temporal and semantic informations, have intensified. Publishing these trajectories without adequate privacy safeguards significantly threatens user semantic trajectory privacy. Current methodologies for protecting semantic trajectory privacy do not adequately consider the similarity in query probability between sensitive and alternative semantic locations, thus undermining the efficacy of privacy protection. To address this critical gap, we introduces a novel trajectory privacy protection method based on sensitive semantic location replacement (SSLR), which strategically replaces sensitive semantic locations. This method consists of three key steps: first, semantically annotating trajectory sampling locations to identify sensitive semantic points; second, employing a unique double semicircular area to define replacement regions and selecting replacement Points of Interests (PoIs) that align in semantic attributes and query probabilities; third, reconstructing and publishing the modified trajectories. Our simulation results confirm that SSLR advances the state of the art by delivering superior performance in average recognition rate, geographic similarity, and semantic similarity compared to existing methods. Crucially, by incorporating considerations of query probability similarities, this paper not only enhances the effect of trajectory privacy protection but also preserves its utility. This dual enhancement represents a groundbreaking approach to trajectory privacy, with significant implications for advancing privacy strategies in LBSs within SIoVs.

Privacy protection and utility trade-off for social graph embedding

In graph embedding protection, deleting the embedding vector of a node does not completely disrupt its structural relationships. The embedding model must be retrained over the network without sensitive nodes, which incurs a waste of computation and offers no protection for ordinary users. Meanwhile, the edge perturbations do not guarantee good utility. This work proposed a new privacy protection and utility trade-off method without retraining. Firstly, since embedding distance reflects the closeness of nodes, we label and group user nodes into sensitive, near-sensitive, and ordinary regions to perform different strengths of privacy protection. The near-sensitive region can reduce the leaking risk of neighboring nodes connecting to sensitive nodes without sacrificing all of their utility. Secondly, we use mutual information to measure privacy and utility while adapting a single model-based mutual information neural estimator to vector pairs to reduce modeling and computational complexity. Thirdly, by keeping adding different noise to the divided regions and reestimating the mutual information between the original and noise-perturbed embeddings, our framework achieves a good trade-off between privacy and utility. Simulation results show that the proposed framework is superior to state-of-the-art baselines like LPPGE and DPNE.

RAE-VWP: A Reversible Adversarial Example-Based Privacy and Copyright Protection Method of Medical Images for Internet of Medical Things

RAE-VWP: A Reversible Adversarial Example-Based Privacy and Copyright Protection Method of Medical Images for Internet of Medical Things

Generation of Face Privacy-Protected Images Based on the Diffusion Model.

In light of growing concerns about the misuse of personal data resulting from the widespread use of artificial intelligence technology, it is necessary to implement robust privacy-protection methods. However, existing methods for protecting facial privacy suffer from issues such as poor visual quality, distortion and limited reusability. To tackle this challenge, we propose a novel approach called Diffusion Models for Face Privacy Protection (DIFP). Our method utilizes a face generator that is conditionally controlled and reality-guided to produce high-resolution encrypted faces that are photorealistic while preserving the naturalness and recoverability of the original facial information. We employ a two-stage training strategy to generate protected faces with guidance on identity and style, followed by an iterative technique for improving latent variables to enhance realism. Additionally, we introduce diffusion model denoising for identity recovery, which facilitates the removal of encryption and restoration of the original face when required. Experimental results demonstrate the effectiveness of our method in qualitative privacy protection, achieving high success rates in evading face-recognition tools and enabling near-perfect restoration of occluded faces.

A Performance Efficient Quadrant-Based Scheme for Multiple Assets Location Preservation in Wireless Sensor Networks

The usefulness of wireless sensor networks has grown throughout numerous areas, many of which demand constant monitoring of the event of interest by the sensor nodes. The wireless transfer of information and the connection of the sensors, however, present the network with a number of security risks. A real-world concern is a danger to source location privacy for networks when several events of interest are monitored. In this research, we present the quadrant-based boundary routing (QBR) method for multiple source location privacy protection. In the first phase, the packet is routed arbitrarily away from the source nodes by random routing, and then quadrant routing begins. Before being sent to the base station using a forward random walk, the packet is transmitted to either side of the network border. We present a theoretical analysis of the maximum delay for the proposed technique. Extensive simulations are carried out and results demonstrate that in contrast to Shortest Path Routing, QBR elevates security by 880.95%, entropy by 623.28%, and capture rate by 71.42% for multiple assets, whereas contemporary PRLPRW increases the corresponding metrics by 692%, 390%, and 49.28%, respectively.