Privacy Leakage Problem Research Articles

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

Secure Authentication and Key Agreement Protocol for Cloud-Assisted Industrial Internet of Things

With the expansion of the Industrial Internet of Things (IIoT), real-time data collected by smart sensors deployed in factories are shared over open channels , which may cause unauthorized access of transmitted messages by adversaries, thus causing the problem of privacy leakage. User authentication is the first line of defense for security protection in the IIoT environment. In this paper, we propose a cloud—assisted authentication scheme based on Chebyshev polynomial encryption, in which only authorized users can access the sensing devices in the Internet of Things (IoT) to obtain real-time data. The scheme uses fuzzy extraction technology to verify biometric characteristics. There are three factors to verify the user’s login request: the smart card, password and the user’s personal biometrics. The commonly adopted formal security analysis, the ROR model, is applied to prove the semantic security of session key, and a detailed informal security analysis is performed to show that the proposed scheme can withstand multiple known attacks. Compared with other related user authentication schemes, the proposed scheme provides several extra functionality features, including offline sensor node registration, updating user passwords and biometrics, adding new sensor node deployment, user anonymity and untraceability. In addition, the cost of computation, communication and security is compared with similar schemes, and results show that our scheme has more security performance while the cost is acceptable.

A Privacy-Aware Electricity Consumption Data Collection Model Based on Group Blind Signature

Blockchain gives a new method for distributed data ledgering. The smart grid obtains efficient two-way data transmission and information control. It effectively monitors and regulates the grid by collecting real-time electricity consumption data of users. However, online data collection brings privacy leakage. To solve the problem of privacy leakage in the electricity data collection in the smart grid, a privacy-aware electricity data collection model is proposed. Firstly, we propose a new group blind signature scheme by introducing the blind feature into the identity-based encryption method. Secondly, by applying the proposed group blind signature scheme to the electricity data collection process, we propose a privacy-aware electricity data collection model. The proposed model ensures the conditional anonymity and traceability of user identity and the privacy protection and unforgeability of electricity consumption data.

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission.

A Privacy Protection Scheme for Cross-Chain Transactions Based on Group Signature and Relay Chain

Recently, with the rapid development of blockchain technology, the information interaction and value transfer problems between different blockchains have become the focus of research. The cross-chain technology is to solve the cross-chain operation problems of assets and data between different chains. However, the existing cross-chain technology has the problem of identity privacy leakage. Therefore, this article proposes a cross-chain privacy protection scheme for consortium blockchains based on group signature, certificate authority and relay chain. The scheme is divided into three cross-chain service layers, called the management layer, the transaction layer, and the group layer. The management layer is responsible for the forwarding of cross-chain transactions, the transaction layer includes the blockchains that actually participate in cross-chain transactions, and the group layer is responsible for group signature related work. Through this scheme, the identity privacy of both parties to the transaction can be protected during the cross-chain transaction process.

Internet of Things Device Identification Algorithm considering User Privacy.

The Internet of Things has become the third wave of the information industry and cloud computing, big data, and Internet technologies. Among the many identification technologies used in the Internet of Things, radiofrequency identification technology is undoubtedly one of the most popular methods today. It is replacing the traditional contact IC card and becoming a new trend of smart cards. At the same time, a large amount of data is generated in the IoT environment. A lot of data involve user privacy, and users do not have good control over these data. Collecting and utilizing these data on the basis of protecting user privacy have become an important problem to be solved urgently. With the implementation of the strategy of rejuvenating the country through science and education, major colleges and universities are developing rapidly through enrollment and expansion, which also brings inconvenience to campus security management. Although the traditional campus all-in-one card system can guarantee the security identity of people entering and leaving, it does not reasonably integrate and utilize this information, resulting in waste of information resources and, to a certain extent, the problem of user privacy leakage. To solve the above problems, a new system was developed to integrate resources to identify users. To protect the privacy data of Internet of Things users, a specific solution using blockchain technology is proposed; for the identity authentication problem of Internet of Things users, the identity authentication based on the public key address of the blockchain is used on the chain, and the group signature is used off the chain. The identity authentication method solves the contradiction between anonymity and traceability in blockchain application scenarios. The simulation results show that the system not only considers user privacy but also has extremely important practical significance for the promotion of Internet of Things and RF applications.

A Novel Identity-Based Mutual Authentication Scheme for Vehicle Ad Hoc Networks

The vehicle ad hoc network (VANET) is an emerging industry that deeply integrates a new generation of network communication technology with automotive and road transportation. As the basic nodes of VANETs, vehicles can communicate and share information with other peer vehicles. However, with the rapid development of the industry, the security risks of VANETs, especially the problem of privacy leakage, have become increasingly prominent. To solve this problem, we propose a novel identity-based mutual authentication scheme (IBMA) for vehicle ad hoc networks. In this scheme, we adopt identity-based cryptography (IBC) to generate keys, which reduces the storage burden of the central authority and eliminates the need to rely on the certificate to distribute the public key. Meanwhile, the key escrow issue can be solved, which is a common problem in IBC. Then, the scheme adjusts central authority to semihonest and realizes unconditional privacy protection. In addition, our scheme achieves complete anonymity, which can prevent any other entities such as peer vehicles and central authorities from tracking vehicles. Finally, our scheme provides efficient traceability while protecting vehicle privacy. Security analysis shows that the proposed scheme satisfies a variety of security requirements such as anonymity, reliability, and nonrepudiation. Performance analysis demonstrates that our proposed scheme is efficient and requires less communication and storage cost compared with related schemes.

Protecting Check-In Data Privacy in Blockchain Transactions with Preserving High Trajectory Pattern Utility

Because the blockchain is secure and untamperable, it has been widely used in many industries, such as the financial industry, digital tokens, and e-commerce logistics. The remarkable security feature of the blockchain is that the blockchain verifies the transaction initiated on each block through the node, and its process is broadcast throughout the whole network to let everyone know. On the one hand, this ensures the security of every transaction, but on the other hand, it is easy to cause privacy disclosure problems for transaction users. Therefore, under the premise of ensuring the security of the blockchain, it has become a hot issue to protect the sensitive information of transaction users. A check-in privacy protection (CPP) algorithm based on check-in location generalization is proposed in this paper, which can be applied to blockchain transactions to solve the privacy leakage problem of transaction users’ sensitive information. CPP algorithm not only protects the privacy of check-in data but also keeps the high utility of trajectory pattern data. Firstly, location types are recommended in the sensitive check-in location generalization based on the user’s trajectory pattern by using Markov chain technology. Secondly, to make sure that the generalized locations can be scattered as much as possible to prevent the attacker from deducing back, a heuristic rule is designed to select the generalized location based on the recommended location types, and at the same time, the similarity between the anonymous trajectory and the original trajectory is maintained. In addition, a generalized location search strategy is designed to improve the efficiency of the algorithm. Based on the real spatial-temporal check-in data, the results of the experiment indicate that our algorithm can effectively protect the privacy of sensitive check-in while ensuring the high utility of trajectory pattern data.

A Targeted Privacy-Preserving Data Publishing Method Based on Bayesian Network

Privacy-preserving data publishing (PPDP) is an essential prerequisite for data-driven AI technologies, (such as data mining, machine learning, deep learning, etc.) to extract knowledge from data safely and legally. It has, as it should be, been studied and explored as a hot topic in the last decade. However, existing privacy protection mechanisms cannot take into account the following three aspects: preventing background attack, maximizing data availability, and resisting sensitive information mining. In this work, we propose a novel privacy-preserving data publishing framework, which protects privacy by releasing simulated data instead of real data. It is explored for generating data similar to the distribution of the real data by using Bayesian network. It consists of two ingredients. First, we transform the problem of data publication into the generation process of a Bayesian network, and correspondingly, the problem of privacy leakage is transformed into one kind of Bayesian inference attack. Second, we propose a re-anonymity framework, named (d, L)-injection, which flexibly resolves the impact of increased privacy protection strength on data availability. In addition, we transplant three classical privacy-preserving strategies to the generated Bayesian network, and demonstrates the effectiveness of the method through three public data sets from multiple application domains.

Security Data Sharing of Shipbuilding Information Based on Blockchain

The shipbuilding industry has problems such as long transaction cycles, many participating suppliers, and data sensitivity. A time-dimensional shipbuilding information security sharing scheme based on an alliance chain is proposed to solve this problem. The blockchain can better deliver value and protect user privacy, the blockchain can directly complete the instant transfer of value through smart contracts and tokens on the blockchain. We divide the program into three parts: data preprocessing, data storage, and data sharing. For data sensitivity, data confidentiality and reliability are handled separately. Aiming at the problem of user privacy leakage in data storage, a privacy-protecting blockchain shared storage solution is proposed; for deduplication, two deduplication algorithms between storage nodes and within storage alliances are proposed to ensure Data information is not disclosed. Storage nodes are allowed to perform data deduplication, data sharing and down-loading, and use off-chain transactions to solve the blockchain performance problems caused by frequent transactions. The scheme in this paper carries out theoretical analysis from three aspects of correctness, security and performance. Finally, this paper has carried out a comparative experimental analysis on the scheme, and the experimental results show that the proposed scheme is feasible and efficient.

A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

Blockchain has a profound impact on all areas of society by virtue of its immutability, decentralization and other characteristics. However, blockchain faces the problem of data privacy leakage during the application process, and the rapid development of quantum computing also brings the threat of quantum attack to blockchain. In this paper, we propose a lattice-based certificateless fully homomorphic encryption (LCFHE) algorithm based on approximate eigenvector firstly. And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme, which is composed of the private key together with the secret value selected by the user, thus effectively avoiding the problems of certificate management and key escrow. Secondly, we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm, which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount, effectively protecting the transaction privacy of users and having the ability to resist quantum attacks. Finally, we analyze the correctness and security of LCFHE algorithm, and the security of the algorithm reduces to the hardness of learning with errors (LWE) hypothesis.

Privacy-Preserving Image Watermark Embedding Method Based on Edge Computing

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent.

Differential Privacy for Deep and Federated Learning: A Survey

Differential privacy (DP) is considered a de-facto standard for protecting users' privacy in data analysis, machine, and deep learning. Existing DP-based privacy-preserving training approaches consist of adding noise to the clients' gradients before sharing them with the server. However, implementing DP on the gradient is not efficient as the privacy leakage increases by increasing the synchronization training epochs due to the composition theorem. Recently researchers were able to recover images used in the training dataset using Generative Regression Neural Network (GRNN) even when the gradient was protected by DP. In this paper, we propose two layers of privacy protection approach to overcome the limitations of the existing DP-based approaches. The first layer reduces the dimension of the training dataset based on Hensel's Lemma. We are the first to use Hensel's Lemma for reducing the dimension (i.e., compress) of a dataset. The new dimensionality reduction method allows reducing the dimension of a dataset without losing information since Hensel's Lemma guarantees uniqueness. The second layer applies DP to the compressed dataset generated by the first layer. The proposed approach overcomes the problem of privacy leakage due to composition by applying DP only once before the training; clients train their local model on the privacy-preserving dataset generated by the second layer. Experimental results show that the proposed approach ensures strong privacy protection while achieving good accuracy. The new dimensionality reduction method achieves an accuracy of 97%, with only 25 % of the original data size.

A Survey of Privacy Preservation for Deep Learning Applications

Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly reviews the privacy leakage problems that exist when using deep learning, then introduces deep learning algorithms that support privacy protection, compares and looks forward to these algorithms, and summarizes this aspect’s development.

Efficient and Secure Cross-Domain Sharing of Blockchain Electronic Medical Records Based on Edge Computing

In this article, we analysed the problems of electronic medical records (EMRs) and found that the EMRs generated by different hospitals for the same patient are mutually independent and duplication and data sharing are difficult among hospitals. In order to solve this problem, this paper proposes an efficient and secure cross-domain sharing scheme of EMRs based on edge computing. The program allows the doctor to access the personal history EMRs through the patient’s authorization so that the doctor can understand the patient’s history of illness and, on this basis, generate a new medical record for the patient. Then, the doctor sends the EMRs to the edge server, and the server calculates the ciphertext and adds it to the patient’s personal medical record to complete the case update. Analysis shows that this solution can effectively prevent data tampering and forgery through blockchain and avoid privacy leakage problems in plaintext sharing by using searchable encryption and by relying on edge servers to solve nearby computing tasks and divert the computing capacity of cloud servers to improve efficiency. The security proof shows that the scheme satisfies the complex problem of the BDH assumption. Performance analysis shows that the scheme is feasible and efficient.

Automatic Algorithm for Fractal Plant Art Image Similarity Feature Generation

With the popularity of smart devices and the Internet, the volume of multimedia data is growing rapidly, and content-based image retrieval (CBIR) can search for similar images from large-scale images to realize the utilization of the data. For data owners, outsourcing the management and maintenance of image data to cloud service providers can effectively reduce costs, but there is a privacy leakage problem. In this paper, we focus on image feature extraction, index design, and image similarity recognition methods under a dual server model with content-based image security similarity recognition as the research topic, the work done such as proposing a BOVW (Bag of Visual Word) feature-based image security similarity recognition scheme. The scheme combines SIFT (scale-invariant feature transform) feature secure extraction and locally sensitive hashing algorithm to achieve secure extraction of BOVW features of images. To protect the BOVW features of images, an inverted index based on word frequency division is designed, the index is stored in chunks, and an image secure similarity recognition scheme based on CNN (convolutional neural networks) features is proposed. The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. The security and performance of the proposed scheme are theoretically analyzed and experimentally verified. Based on different image datasets, the impact of different parameters on the performance of the scheme is tested, and optimized parameters are given. The experimental results show that the proposed scheme in this paper can effectively improve the efficiency of analyzing the similarity of plant botanical art images compared to the existing schemes.

A secure aggregation routing protocol with authentication and energy conservation

AbstractWireless sensor networks (WSNs) have emerged in various applications. However, the uneven resource allocation of sensor devices seriously affect the lifetime of wireless sensor networks. Moreover, the privacy leakage problem in wireless sensor networks is becoming increasingly serious. To solve the imbalance of energy consumption and privacy disclosure of nodes, we propose a secure aggregation routing protocol with authentication and energy conservation. Firstly, a new cluster head and cluster gateway selection method is proposed to balance the energy consumption of each node. By designing cost function and node density function, a cluster head and cluster gateway selection indicator is defined, and furthermore, a cluster head and cluster gateway node selection method is proposed. Secondly, a key negotiation scheme with authentication function is proposed. By introducing a trusted authorized node, a node authorization method is proposed to distribute the authorized keys. Finally, by introducing the proposed cluster head selection method and key negotiation scheme into WSN, a secure aggregation routing protocol with authentication and energy conservation is proposed. Performance analysis shows the proposed protocol obtains more executable rounds with less total energy consumption and extend the lifetime of wireless sensor networks.

Privacy information verification of homomorphic algorithm for aggregated data based on fog layer structure

Becoming a vital position in the interconnection industry of the Internet of Things, IIoT has promoted the conversion of traditional industries to intelligent industries. However, it is necessary further to solve the security and privacy threats in IIoT while reducing communication bandwidth and computing resource consumption to carry out research. In order to solve the problem of user privacy leakage caused by the access structure, a fog computing-oriented access control structure hiding scheme is proposed in the paper. The Paillier homomorphic filter algorithm is introduced in the fog calculation. The Paillier homomorphic algorithm hides the mapping function in the access structure during the data upload process, achieving the effect of completely hiding the access structure. Moreover, the ciphertext is stored separately from the access structure, and the Paillier homomorphic algorithm is run through the fog node during the decryption process to detect whether the attributes of the data user exist in the hidden access structure. If it exists, reconstruct the mapping function and send it to the data user, who then downloads and decrypts the ciphertext. If it does not exist, it means that the data user does not meet the access conditions of the data, and there is no need to download and decrypt the ciphertext. Meanwhile, a simulation experiment platform is constructed in the paper to distinguish the performance of the method proposed in the paper from other similar methods, proving the efficiency and practicability of the scheme.

Privacy-Preserving Distributed Optimal Power Flow With Partially Homomorphic Encryption

Distribution grid agents are obliged to exchange and disclose their states explicitly to neighboring regions to enable distributed optimal power flow dispatch. However, the states contain sensitive information of individual agents, such as voltage and current measurements. These measurements can be inferred by adversaries, such as other participating agents or eavesdroppers, leading to the privacy leakage problem. To address the issue, we propose a privacy-preserving distributed optimal power flow (OPF) algorithm based on partially homomorphic encryption (PHE). First of all, we exploit the alternating direction method of multipliers (ADMM) to solve the OPF in a distributed fashion. In this way, the dual update of ADMM can be encrypted by PHE. We further relax the augmented term of the primal update of ADMM with the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm regularization. In addition, we transform the relaxed ADMM with the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm regularization to a semidefinite program (SDP), and prove that this transformation is exact. The SDP can be solved locally with only the sign messages from neighboring agents, which preserves the privacy of the primal update. At last, we strictly prove the privacy preservation guarantee of the proposed algorithm. Numerical case studies validate the effectiveness and exactness of the proposed approach. In particular, the case studies show that the encrypted messages cannot be inferred by adversaries. Besides, the proposed algorithm obtains the solutions that are very close to the global optimum, and converges much faster compared to competing alternatives.

An Access Control Scheme Supporting Privacy Protection Based on Blockchain and Attribute

With the large-scale application of cloud storage systems, lots of attribute-based access control (ABAC) schemes have been introduced to protect data and user security in this insecure environment. ABAC can make data owners control their own data and protect data security and privacy. However, there are two serious privacy leakage problems, namely user attribute privacy and policy privacy, should be solved in the process of constructing ABAC schemes. In this paper, an ABAC scheme supporting privacy protection is constructed, which can solve privacy leakage problems of user attribute privacy and policy privacy in large universe attribute system. Furthermore, a framework is proposed. The framework combines our ABAC scheme, the Ethereum blockchain and blockchain-based storage system. In this framework, the security characteristics of blockchain technology are used to realize decentralization, tamper-resistant and avoiding single point of failure. Besides, the problems of attribute revocation and policy updating are solved by smart contract on the Ethereum blockchain. Finally, we established an initial implementation on Linux and Rinkeby test network, and the experimental results show that our scheme is feasible.