Privacy Protection Method Research Articles

The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and Overviews

Abstract This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users’ privacy protection, i.e., it is required to comprehensively improve the security of users’ preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

Energy trading IoT system based on blockchain

Energy trading technology based on Internet of Things (IoTs) is one of the research hotspots. However, in the process of energy trading, it is easy to leak the user’s personal privacy. In order to solve the problem of privacy leakage in the energy trading IoT system, we explore a secure energy trading method based on blockchain technology and homomorphic encryption technology. Firstly, we propose a homomorphic encryption method for blockchain system. Secondly, we propose a privacy protection method based on blockchain architecture. Finally, we propose an energy trading IoT system based on blockchain. By integrating homomorphic encryption, blockchain and other technologies, we have implemented a secure energy trading system. Compared with other similar systems, our energy trading IoT system based on blockchain can provide more secure user services. In addition, our system can better protect the privacy of users.

Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study.

BackgroundIn clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression—a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information—with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate secure file transfer between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate distributed regression analysis (DRA) with vertically partitioned data, a setting where the data attributes from a cohort of patients are available from different data sources, was unknown.ObjectiveThe objective of the study was to describe the feasibility of using PopMedNet and enhancements to PopMedNet to facilitate automatable vertical DRA (vDRA) in real-world settings.MethodsWe gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We enhanced PopMedNet based on these requirements to improve its technical capability to support vDRA.ResultsPopMedNet can enable automatable vDRA. We identified and implemented two enhancements to PopMedNet that improved its technical capability to perform automatable vDRA in real-world settings. The first was the ability to simultaneously upload and download multiple files, and the second was the ability to directly transfer summary-level information between the data-contributing sites without a third-party analysis center.ConclusionsPopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research in real-world settings.

Differential privacy EV charging data release based on variable window.

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.

A Location Privacy Protection Algorithm Based on Double K-Anonymity in the Social Internet of Vehicles

As an emerging complex social network, the Social Internet of Vehicles (SIoV) potentially exposes user location privacy. In this letter, we propose a location privacy protection method based on double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity that hides user locations and request information. The cloud server is introduced as a trusted third party to isolate the direct communication between users and the service provider, while correlation between identities and requests is also reduced by means of a permutation and combination method. Extensive simulations are conducted to demonstrate that the method can protect user location privacy to the greatest extent possible while still ensuring service availability.

Privacy protection of medical data in social network

BackgroundProtection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data.MethodsPrivacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset.ResultsThe associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a, i.e. k = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3.ConclusionWe showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.

Towards an Architecture-based Ensemble Methods for Online Social Network Sensitive Data Privacy Protection

&lt;p&gt;&lt;strong&gt;Abstract - &lt;/strong&gt;In 2014, the world woke up to a giant data breach that leveraged users’ personal information that was taken from one of the world’s biggest social network platform. Based on the literature, this was possible because of the Centralised Architectural-based Approach to protecting the privacy of users’ online data. Although the literature is inundated with decentralized approaches, there is none to the best of our knowledge that uses an ensemble of methods and draws on a consensus mechanism to address the challenges caused by the Centralised Architectural-based Approach. This paper presents a decentralized approach that adopts and adapts an ensemble of methods. These methods include cryptographic, hashing, and the plenum byzantine fault tolerance algorithms that present a consensus platform, protocol, and mechanism to use the technology of blockchain in a novel manner as a significant contribution. This paper adopts the descriptive approach in its presentation as the usable implementation of the presented proposal is near completion with issues of computational overhead addressed based on preliminary results that show promise of being able to support agreement up to ≈ 75% in terms of making changes by participants in the chain. &lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication

With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency.

High-Performance Secure Access and Data Privacy Protection Method for 5G Border Routing Gateway

With the substantial increase in the amount of data information and the development of 5G technology, boundary computing technology based on 5G, as a real-time and efficient data processing method, has broad application prospects. The aggregation gateway further aggregates the power injection data of all electric energy storage units and sends it to the power company. The power company uses its own private key to access the aggregated data. Row decryption to obtain the total power data recovered in each time slot. The paper proposes a new solution using Lagrangian interpolation to ensure data privacy and security. The security shows that the algorithm can not only protect the privacy of the electric energy storage unit, but also realize the confidentiality and integrity of the injected data. Efficiency analysis shows that this algorithm can significantly reduce computational complexity and improve communication efficiency.

Privacy Protection Method for Sensitive Weighted Edges in Social Networks

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

KPDR : An Effective Method of Privacy Protection

To solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of information points is analyzed by using the characteristics of Dirichlet graph, and the anonymous set of users is generated and sent to LBS server. Second, the relationship matrix is generated, and the proximity relationship between the user position and the target information point is obtained by calculation. Then, the private information retrieval model is applied to ensure the privacy of users’ target information points. Finally, the experimental results show that the KPDR method not only satisfies the diversity of l 3 / 4 , but also increases the anonymous space, reduces the communication overhead, ensures the anonymous success rate of users, and effectively prevents the disclosure of user privacy.

A trajectory data publishing algorithm satisfying local suppression

Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data. Therefore, we propose a trajectory data differential privacy protection algorithm based on local suppression Trajectory privacy protection based on local suppression (TPLS) to provide the user with the ability and flexibility of protecting data through local suppression. The main contributions of this article include as follows: (1) introducing privacy protection method in trajectory data release, (2) performing effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set, and (3) proposing a differential privacy protection algorithm based on local suppression. In the algorithm, we achieve the purpose Maximal frequent sequence (MFS) sequence loss rate in the trajectory data set by effective local inhibition judgment and updating the minimum violation sequence set, and then establish a classification tree and add noise to the leaf nodes to improve the security of the data to be published. Simulation results show that the proposed algorithm is effective, which can reduce the data loss rate and improve data availability while reducing the risk of user privacy leakage.

Privacy Protection Method of Electric Power Network Based on Blockchain

In order to ensure the integrity and confidentiality of power data in the era of big data, it is very important to protect the privacy of smart grid data information. Analyzing electricity big data can obtain electricity consumption habits of electricity users to guide the formulation of power supply plans. This experiment uses an asymmetric encryption algorithm. The encryption and decryption of the asymmetric encryption algorithm use two different keys to fully guarantee the security of the blockchain system. The experiment is based on the blockchain technology in photovoltaic microgrid electricity trading, which belongs to the current type of trading, which is divided into five stages. The service provider is a node of the blockchain system. All smart contracts are encrypted by the private key of both parties in the transaction and the public key of the service provider, which fully guarantees the privacy of users. The experimental data shows that the smart contract is decrypted by the private key and the public key of both parties, so as to obtain the relevant information of the transaction and make the corresponding power dispatch work during the transaction period.The blockchain system solves the security and privacy issues of both parties in the absence of a third-party intermediary. Therefore, the use of blockchain technology can achieve a higher degree of automation for point-to-point power transactions.

Data Sharing and Privacy in Pharmaceutical Studies.

Adverse drug events have been a long-standing concern for the wide-ranging harms to public health, and the substantial disease burden. The key to diminish or eliminate the impacts is to build a comprehensive pharmacovigilance system. Application of the "big data" approach has been proved to assist the detection of adverse drug events by involving previously unavailable data sources and promoting health information exchange. Even though challenges and potential risks still remain. The lack of effective privacy-preserving measures in the flow of medical data is the most important Accepted: one, where urgent actions are required to prevent the threats and facilitate the construction of pharmacovigilance systems. Several privacy protection methods are reviewed in this article, which may be helpful to break the barrier.

Method of Differential Privacy Protection for Web-Based Communities Based on Adding Noise to the Centroid of Positions

Method of Differential Privacy Protection for Web-Based Communities Based on Adding Noise to the Centroid of Positions

Method of differential privacy protection for web-based communities based on adding noise to the centroid of positions

The research of location privacy protection has a very important significance for the emergence and sustenance of web-based communities in the big data era. The method of differential privacy for position is a privacy model strictly for the measurement of privacy. However, under the privacy protection for multi-positions, it will bring much bigger errors by adopting the method of adding noise to protect the privacy of multi-positions. In this paper, the author puts forward a new method of differential privacy for multi-positions based on mechanism of adding noise to the centroid of positions and compares with the independent mechanism of adding noise. The experiment shows that the centroid mechanism of adding noise is better than the mechanism of independently adding noise.

IFTS: A Location Privacy Protection Method Based on Initial and Final Trajectory Segments

Privacy protection problem is one of the most concerning issues related to Location-Based Services (LBS) in our daily life. Privacy protection of LBS often requires anonymizing customer's trajectory data. Currently available methods for trajectory anonymity often assume an entire trajectory as one anonymous unit, which may lead to low anonymity efficiency due to the massive amount of trajectory data, especially for customers travel through a long road. Considering people's routine activities, the starting and ending locations of a trip often uncover the user's request intent, which may lead to exposure of user privacy. In order to address the problem of inefficient trajectory anonymity, we propose a location privacy protection method that is based on the initial and final trajectory segmentation (IFTS) in this paper. In the IFTS method, the road network structure is first transformed into an edge cluster model based on its location type. Then, the user trajectory is divided to segments according to the temporal sequence of the ingress and egress nodes. The initial and final trajectory segments are identified and divided into equivalence classes, which then are used for constructing a trajectory graph and the corresponding k-anonymous set. Our experimental results show that the proposed method can reduce the anonymous area, improve anonymity efficiency, and enhance trajectory data utilization compared to the existing methods for trajectory anonymity.

A Face Occlusion Removal and Privacy Protection Method for IoT Devices Based on Generative Adversarial Networks

The device group based on the Internet of Things (IoT) has been used in face recognition in real life, so it is more necessary to discuss the current data security issues and social hot issues. The Internet of Things device combines edge conditions and many recognizers to generative adversarial networks. On the premise of meeting the needs of partial occlusion of users, face recovery is completed through information reorganization. CelebA training set is used to simulate face occlusion, and the model is trained and tested. The results show that the method can recover the complete image of the protection for the facial privacy of specific people. At the same time, the IoT device using this method ensures that the face information is not easy to have tampered with when attacked.

Hyper-Noise Interference Privacy Protection Framework for Intelligent Medical Data-Centric Networks

Convolutional neural network (CNN) has been widely used in the medical domain, but how to protect privacy is still a challenge. In addition, traditional differential privacy protection methods are very sensitive to noise; hyper-scale noise has a serious effect on the validity of the network. In this article, therefore, a novel framework named improved private aggregation of teacher ensembles (IPATE) is proposed. IPATE aims to protect the privacy of trained models and medical data by adding hyperscale Laplace noise to the extended voting difference of many CNN models. An experimental example including 12 leads data of 148 myocardial infarction patients and 52 normal subjects was used in the study. Experimental results show that with the increase of noise and under the condition of limited data, IPATE can still achieve about 90 percent accuracy, and it is not sensitive to noise.

A k-Anonymous Location Privacy Protection Method of Polygon Based on Density Distribution

In order to solve the problem of out-off-balance caused by accuracy of location information between privacy protection security and query service quality, considering basic information comprehensively such as the environment and geographical features and so on, and adopting k- anonymous privacy protection mechanism, we present a k-anonymous location privacy protection method of polygon based on density distribution. Firstly, a k-anonymous irregular polygon region is structured in whole area. Then, according to the preset anonymous region and density threshold, the better effects of anonymous are obtained by expanding the region or adding the random dummy locations. Experimental results show that the proposed method improves the efficiency of anonymous and query accuracy. The balance between privacy protection security and query quality is achieved.