Privacy Protection Scheme Research Articles

Research on machine learning technology with privacy protection strategy in recommendation field

Technologies such as machine learning can achieve accurate personalized recommendations. However, due to the collection and utilization of a large amount of user information in this process, people are widely worried about data security and privacy issues. This paper first introduces two key issues of privacy protection in the field of machine learning, namely data privacy and model privacy. On this basis, this paper introduces and analyzes homomorphic encryption, differential privacy and federated learning, and compares their advantages and disadvantages. Among them, homomorphic encryption technology has a large computational cost, differential privacy technology has a negative impact on system accuracy, and federated learning technology has a high training and communication cost. Therefore, it will be the future research direction to study more efficient and accurate recommendation models.

Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.

Smart meter privacy control strategy based on multi‐agent hidden Markov energy management model under low trust communication

AbstractWith the popularity of smart meters, the frequent information exchange between smart grids and consumers leads to easy leakage of consumers' electricity consumption data. These leaked electricity consumption data are obtained by some malicious attackers and used to infer consumers' behavioural patterns by non‐intrusive load monitoring (NILM), which seriously threatens consumers' privacy. Therefore, the multi‐agent Hidden Markov energy management model is proposed in this paper to safeguard the privacy of consumers. First, a weighted Bayesian risk model is proposed, which combines privacy leakage risks and energy storage system (ESS) losses in a microgrid with multiple agents. Next, a three‐loop model for lithium batteries is constructed to quantify the capacity degradation and cost issues of the ESS. Finally, the multi‐objective optimization problem is resolved by integrating the Bayesian risk model with a hidden Markov model to simulate attackers. The proposed multi‐agent Markov decision process method is validated on Electricity Consumption and Occupancy (ECO) dataset, and control strategies are evaluated based on different weights in the Bayesian risk model. The results demonstrate that by incorporating the multi‐agent approach and energy storage system capacity degradation into the privacy protection strategy, the lifespan of the energy storage system can be significantly increased.

Location privacy protection of nodes in the perception layer of the Internet of things based on Radio Frequency Identification location aware algorithm

AbstractTo ensure the security of node location privacy in the perception layer of the Internet of Things, this study proposes a location privacy protection method based on RFID location awareness algorithm. This method first introduces an adaptive multi tree anti‐collision algorithm based on collision trees when perceiving and locating the location information of nodes in the perception layer of the Internet of Things, to prevent signal collisions between multiple readers and writers. Then, based on the obtained node location results, a IoT perception layer node location privacy protection scheme based on virtual ring strategy is used to randomly initiate directed co hop routing using the virtual nodes generated after random walking, avoiding the visible area of the source node location, and transmitting data to the aggregation node in the form of the shortest routing path to protect the privacy and security of the node location. Finally, experiments are carried out to prove the progressiveness of the proposed method. The experimental results show that this method can prolong the security period of the source and aggregation nodes in the perception layer of the Internet of Things, protect the privacy and security of node positions, and has great application value.

BFOD: Blockchain-Based Privacy Protection and Security Sharing Scheme of Flight Operation Data

BFOD: Blockchain-Based Privacy Protection and Security Sharing Scheme of Flight Operation Data

Efficient privacy protection scheme with batch verification in smart grid

Smart grids can establish two-way communication with users, collect their electricity consumption data and provide reasonable pricing, but fine-grained electricity consumption data leads to the leakage of user privacy. In order to protect the privacy of user data and the security of data transmission process, this article proposes an efficient and batch validated privacy protection scheme. In this scheme, legitimate entities transmit encrypted electricity data after mutual authentication. To ensure the correct data is received, we propose a new batch signature verification algorithm. Security analysis shows that our solution achieves user privacy and data security. The simulation experiments provided demonstrate that the efficiency of the proposed scheme can satisfy the requirements of real-time communication.

Research on Data Security and Privacy Protection Strategies in Hospital Information Management

Abstract Hospital information security, especially the management of hospital information, is of great significance to improve hospital quality, promote resource sharing, and enhance hospital competitiveness. Despite their unique advantages in preventing transmission data leakage when dealing with medical data, federated learning algorithms still have some shortcomings. Based on this, this study proposes to combine the improved TVFedmul algorithm with the federated learning technique to enhance the efficiency of information aggregation and also proposes to utilize the Gaussian difference privacy algorithm to enhance the protection of private data. Four datasets from cancer rehabilitation data are utilized as research samples in experiments. Compared with the FedAvg algorithm, the TVFedmul algorithm is relatively leading in accuracy, e.g., the accuracy enhancement on the same-distribution dataset of renal cancer reaches 3.03%, and the performance enhancement in the C-domain of the non-simultaneous-distribution dataset of breast cancer reaches 14.2%. The TVFedmul algorithm’s model aggregation speed is also faster, which can effectively improve the efficiency of information aggregation. Although the privacy mechanism of the Gaussian differential privacy algorithm affects the accuracy of the model, its accuracy convergence is not much different from that of federated learning without differential privacy, implying that the Gaussian differential privacy algorithm utilizes a small performance loss to provide more valuable privacy protection.

Blockchain-Enhanced Data Privacy Protection and Secure Sharing Scheme for Healthcare IoT

Blockchain-Enhanced Data Privacy Protection and Secure Sharing Scheme for Healthcare IoT

V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm

V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm

Research on transparent management of economic transactions using blockchain technology

Abstract The data asset trading market is gradually emerging with the development of the digital economy, making the development of a safe and effective data asset trading management system an important research direction. This paper uses blockchain technology to create a transparent management system for economic transactions. A PHRP algorithm is proposed to guarantee the security of the transaction, addressing the privacy protection issue involved in the transaction process. The PHRP algorithm calculates the actual transaction amount by encrypting both the input and output amounts, utilizing the additive homomorphic property. It also ensures the undeniability and integrity of the transaction by encrypting and committing random numbers. The relevant test results show that the transaction privacy protection scheme based on the PHRP algorithm can better meet the requirements of the economic transaction management system. Users’ satisfaction with the system constructed in this paper in terms of perceived usefulness and perceived ease of use scored high on average, 4.23 and 4.17, respectively, and expressed average for security, for which this paper proposes relevant improvement strategies.

Privacy Risks and Regulatory Challenges in Smart Grids and Renewable Energy Systems

Smart grid technologies are central to the global shift towards a more efficient and sustainable energy infrastructure, integrating advanced digital systems with traditional power networks. While these technologies offer significant benefits, including enhanced energy management and the seamless integration of renewable energy sources, they also introduce complex privacy challenges. The extensive data collection and real-time communication capabilities inherent in smart grids raise concerns over consumer privacy, data breaches, and cybersecurity threats. This paper critically examines these privacy risks within the context of evolving regulatory frameworks such as the GDPR, NIS2 Directive, and the forthcoming EU AI Act. The discussion emphasizes the need for innovative privacy protection strategies. Furthermore, the paper explores the role of anticipatory regulation in addressing the ethical dilemmas posed by AI and blockchain technologies in smart grids. By offering a forward-looking analysis of these challenges, the paper provides actionable insights for policymakers, energy companies, and technologists on balancing innovation with privacy protection. The conclusion highlights the necessity of international cooperation in establishing robust privacy standards that can support the sustainable development of smart grids while safeguarding consumer rights.

Research on android user privacy permission analysis and protection mechanism under big data environment

With the rapid development of big data technology, the issue of user privacy security on the Android platform is becoming increasingly prominent. This paper aims to conduct an in-depth analysis of the privacy permissions of Android users under the big data environment and explore effective protection mechanisms. Through research on permission management, application behavior, and user privacy leakage pathways in the Android system, this paper proposes a comprehensive privacy protection strategy to enhance the privacy security level of Android users in the big data environment.

An effective iris biometric privacy protection scheme with renewability

Iris is considered a unique, robust biometric that can be used as an authentication factor. Privacy protection of individuals’ iris templates is an essential requirement. The essential attributes needed to ensure privacy of biometrics are irreversibility, renewability, and unlinkability. Information distortion is an effective way to achieve the privacy. However, this may lead to some degradation of recognition performance. A trade-off between recognition performance and the privacy level provided by the authentication system arises when it tries to provide both simultaneously. This paper proposes a novel approach that ensures the privacy of each individual's iris by distorting iris feature information. At the meantime, this approach balances the trade-off mentioned above up to a tolerable level. The binary feature templates of iris biometrics are transformed into another irreversible binary representation by manipulating them block-wise. The results obtained against two datasets using proposed approach illustrate excessive Genuine Acceptance Rates (GAR) when the approach accepts imposters at a rate which is reduced to 0.01 %. The proposed approach satisfies the three attributes that a biometric system should fulfil, namely irreversibility, renewability, and unlinkability. The renewability attribute induces the cancellability of iris biometric template. Compared to the existing works, the proposed approach maintains Equal Error Rate (EER) at 0.18 %.

SBM-SA: A Safety Beacon Message Separation Algorithm for Privacy Protection in Internet of Vehicles

A safety beacon message (SBM) plays a pivotal role in the Internet of Vehicles (IoV), broadcasting crucial events and road conditions to nearby vehicles. Given the sensitive nature of the data, such as vehicle identity and location, ensuring privacy is paramount. The significance of this research lies in addressing the pressing need for comprehensive privacy protection in the IoV, especially as most existing schemes focus on safeguarding either vehicle identity or location data during exchanges with core servers. The primary objective of this article is to introduce an SBM separation algorithm, termed SBM-SA, designed to holistically protect both identity and location data. Utilising correlation analysis, the SBM-SA stands as an innovative anonymisation privacy algorithm. Through a simulated IoV environment, the accuracy and efficacy of an SBM-SA are meticulously analysed and juxtaposed against prevailing privacy protection schemes. The findings underscore the SBM-SA’s potential to significantly enhance privacy measures in the IoV. Implications of this research extend to shaping future privacy protection strategies of the SBM, emphasising the need for holistic and robust solutions in an increasingly interconnected vehicular landscape.

Cross-Chain Asset Transaction Method Based on Ring Signature for Identity Privacy Protection

In recent years, the rapid development of blockchain technology has facilitated the transfer of value and asset exchange between different blockchains. However, achieving interoperability among various blockchains necessitates the exploration of cross-chain technology. While cross-chain technology enables asset flow between different blockchains, it also introduces the risk of identity privacy leakage, thus posing a significant threat to user security. To tackle this issue, this article proposes a cross-chain privacy protection scheme that leverages ring signature and relay chain technology. Specifically, this scheme utilizes RCROSS contracts based on ring signatures to handle cross-chain transactions, thereby ensuring the privacy of both parties involved in the transaction. This cross-chain solution demonstrates practicality and efficiency in facilitating cross-chain asset trading. Furthermore, it effectively combats reuse attacks and man-in-the-middle attacks at the application layer while also providing resistance against denial-of-service attacks at the network layer. To validate the proposed cross-chain solution, we conducted tests by constructing a specific cross-chain scenario and by focusing on the natural gas consumption values generated by the RCROSS contract function used in the application chain. The findings indicate that our proposed solution is highly practical in safeguarding the identity privacy of transaction participants. This article’s framework guarantees reliability, security, and efficiency in cross-chain asset transactions. By incorporating ring-based signatures and relay chain technology, users can confidently protect their identity privacy, thus ensuring secure and smooth cross-chain transactions.

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology

The advent of the Internet of Vehicles (IoV) has led to a significant surge in data generation from vehicles, prompting the innovative utilization of data transactions within the IoV environment. However, due to the inherent trustless nature of data transactions in IoV, concerns have arisen regarding the lack of trust between involved parties and the potential compromise of user privacy. To address these issues, blockchain technology emerges as a suitable decentralized distributed storage and security management solution, offering transparency and security in data transactions. In this study, we leverage blockchain to integrate with the IoV, devising a robust trust management framework, and devising a privacy protection scheme to safeguard user privacy concerns. Additionally, we employ the performance evaluation process algebra (PEPA) method for system modeling and performance analysis to assess the efficacy of our proposed solution. Empirical findings demonstrate that our approach effectively enhances the performance of data transactions within the IoV while ensuring that the privacy of users remains intact.

Exploring the future application of UAVs: Face image privacy protection scheme based on chaos and DNA cryptography

In the application and promotion of UAV in the future, remote sensing image information is transmitted frequently, and its security issues will not be ignored. Aiming at the privacy security problem of portrait information in remote sensing images, this paper proposes a face privacy protection technology scheme based on chaos and DNA cryptography. Firstly, the edge recognition face detection technology is used to identify the face information, and the corresponding matrix is selected as the object of selective encryption. Then, the hash eigenvalues of the selected encrypted object are extracted, and the plaintext-associated chaotic sequences are generated for RGB permutation and zigzag interleaved scrambling in turn. Then, the three channels of RGB are encrypted by dynamic chain DNA encoding in turn. Finally, the cipher-image is obtained after performing discontinuous diffusion and lightweight bit-level confusion. In this paper, chaotic pseudo-random sequences with plaintext correlation and dynamic DNA chain encryption are used to effectively improve the ability of encryption system to resist cryptographic attacks. At the same time, selective encryption can effectively reduce the encryption complexity and performance overhead. Experimental results and security analysis show that the privacy protection scheme has excellent security and efficiency performance. Therefore, the face privacy protection technology scheme reported in this paper has excellent performance and has broad application prospects in UAV and remote sensing communication.

Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing

The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.

GLPS: A Geohash-Based Location Privacy Protection Scheme.

With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.