User Privacy Protection Method Based on Dynamic Hiding

2017 ◽  
Author(s):  
Yujun Chen ◽  
Yuansheng Lou ◽  
Yong Chen
Keyword(s):  
Privacy Protection ◽  
User Privacy ◽  
Protection Method

scholarly journals A trajectory data publishing algorithm satisfying local suppression

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199340
Author(s):  
Xiaohui Li ◽  
Yuliang Bai ◽  
Yajun Wang ◽  
Bo Li
Keyword(s):  
Privacy Protection ◽  
Loss Rate ◽  
Differential Privacy ◽  
Classification Tree ◽  
Data Availability ◽  
Trajectory Data ◽  
User Privacy ◽  
Data Set ◽  
Privacy Leakage ◽  
Protection Method

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.

scholarly journals A Novel Location Privacy-Preserving Approach Based on Blockchain

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3519 ◽  
Author(s):  
Ying Qiu ◽  
Yi Liu ◽  
Xuan Li ◽  
Jiahui Chen
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
User Participation ◽  
Location Based Services ◽  
Third Party ◽  
User Privacy ◽  
Protection Method ◽  
Location Privacy Protection ◽  
Reward Mechanism

Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.

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

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zongda Wu ◽  
Chenglang Lu ◽  
Youlin Zhao ◽  
Jian Xie ◽  
Dongdong Zou ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Privacy Protection ◽  
User Preference ◽  
User Privacy ◽  
Server Side ◽  
Algorithm Efficiency ◽  
Important Study ◽  
Basic Reference ◽  
Complete Set ◽  
Technical Features

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.

A dummy-based user privacy protection approach for text information retrieval

2020 ◽  
Vol 195 ◽  
pp. 105679
Author(s):  
Zongda Wu ◽  
Shigen Shen ◽  
Xinze Lian ◽  
Xinning Su ◽  
Enhong Chen
Keyword(s):  
Information Retrieval ◽  
Privacy Protection ◽  
User Privacy ◽  
Text Information ◽  
Text Information Retrieval

scholarly journals Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

2018 ◽  
Vol 14 (11) ◽  
pp. 40
Author(s):  
Bohua Guo ◽  
Yanwu Zhang
Keyword(s):  
Data Aggregation ◽  
Privacy Protection ◽  
Random Number ◽  
Polynomial Regression ◽  
Homomorphic Encryption ◽  
Weighted Average ◽  
Base Station ◽  
Wireless Sensor ◽  
Protection Scheme ◽  
Protection Method

<p class="0abstract"><span lang="EN-US">To improve the data aggregation privacy protection scheme in wireless sensor network (WSN), a new scheme is put forward based on the privacy protection of polynomial regression and the privacy protection method based on the homomorphic encryption. The polynomial data aggregation (PRDA+) protocol is also proposed. In this scheme, the node and the base station will pre-deploy a secret key, and the random number generator encrypts the random number for the seed through the private key, which protects the privacy of the data. Then, by comparing the decrypted aggregate data through the correlation between the two metadata, the integrity protection of the data is realized. A weighted average aggregation scheme that can be verified is proposed. In view of the different importance of user information, the corresponding weights are set for each sensor node. EL Gamal digital signature is used to authenticate sensor nodes. The results show that the signature verification algorithm enables the scheme to resist data tampering and data denial, and to trace the source of erroneous data.</span></p>

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