Trajectory Privacy Protection Based on Location Semantic Perception

2019 ◽  
Vol 28 (03) ◽  
pp. 1950006
Author(s):  
Zhao-Wei Hu ◽  
Jing Yang
Keyword(s):  
Privacy Protection ◽  
Information Leakage ◽  
Mean Values ◽  
Privacy Leakage ◽  
Quantitative Calculation ◽  
Protection Method ◽  
Policy Selection ◽  
Protection Efficacy ◽  
Text Query ◽  
Type Information

A personalized trajectory privacy protection method based on location semantic perception to achieve the personalized goal of privacy protection parameter setting and policy selection is proposed. The concept of user perception is introduced and a set of security samples that the user feels safe and has no risk of privacy leakage is set by the user’s personal perception. In addition, global privacy protection parameters are determined by calculating the mean values of multiple privacy protection parameters in the sample set. The concept of location semantics is also introduced. By anonymizing the real user with [Formula: see text] collaborative users that satisfy the different semantic conditions, [Formula: see text] query requests which do not have the exact same query content and contain precise location information of the user and the collaborative user are sent to ensure the accuracy of the query results and avoid privacy-leaks caused by the query content and type. Information leakage and privacy level values are tested for qualitative analysis and quantitative calculation of privacy protection efficacy to find that the proposed method indeed safeguards the privacy of mobile users. Finally, the feasibility and effectiveness of the algorithm are verified by simulation experiments.

scholarly journals An Unequal Image Privacy Protection Method Based on Saliency Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rongxin Tu ◽  
Wenying Wen ◽  
Changsheng Hua
Keyword(s):  
Privacy Protection ◽  
Saliency Detection ◽  
Image Data ◽  
Feature Integration ◽  
Attention Model ◽  
Data Services ◽  
Privacy Leakage ◽  
Protection Method ◽  
Privacy Issues ◽  
Lightweight Encryption

Cloud platforms provide a good stage for storing and sharing big image data for users, although some privacy issues arise. Image encryption technology can prevent privacy leakage and can ensure secure image data sharing on cloud platforms. Hence, in this paper, an unequal encryption scheme based on saliency detection is proposed. First, based on the mechanism of visual perception and the theory of feature integration, the visual attention model is employed to realize the recognition of significant regions and insignificant regions. Then, a dynamic DNA encryption algorithm is proposed to exploit heavyweight encryption for significant regions, while semi-tensor product compressed sensing is introduced to exploit lightweight encryption and compression for insignificant regions. Experimental results demonstrate that the proposed framework can serve to secure big image data services.

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 Adaptive Transformation for Robust Privacy Protection in Video Surveillance

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Mukesh Saini ◽  
Pradeep K. Atrey ◽  
Sharad Mehrotra ◽  
Mohan Kankanhalli
Keyword(s):  
Video Surveillance ◽  
Privacy Protection ◽  
Region Of Interest ◽  
Surveillance Systems ◽  
Security Threats ◽  
Privacy Leakage ◽  
Protection Method ◽  
Global Operations ◽  
Implicit And Explicit ◽  
Privacy Issues

Privacy is a big concern in current video surveillance systems. Due to privacy issues, many strategic places remain unmonitored leading to security threats. The main problem with existing privacy protection methods is that they assume availability of accurate region of interest (RoI) detectors that can detect and hide the privacy sensitive regions such as faces. However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. Further, there are many implicit privacy leakage channels that have not been considered by researchers for privacy protection. We block both implicit and explicit channels of privacy leakage. Experimental results show that the proposed method incurs 38% less distortion of the information needed for surveillance in comparison to earlier methods of global transformation; while still providing near-zero privacy loss.

Data privacy protection technology of wearable-devices

2020 ◽  
pp. 1-8
Author(s):  
Shuangxia Tang ◽  
Kunquan Shi
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Information Leakage ◽  
Wearable Devices ◽  
Security And Privacy ◽  
Information Distortion ◽  
Protection Method ◽  
User Data ◽  
Property Losses

Wearable-devices have developed rapidly. Meanwhile, the security and privacy protection of user data has also occurred frequently. Aiming at the process of privacy protection of wearable-device data release, based on the conventional V-MDAV algorithm, this paper proposes a WSV-MDAV micro accumulation method based on weight W and susceptible attribute value sensitivity parameter S and introduces differential-privacy after micro accumulation operating. By simulating the Starlog dataset and the Adult dataset, the results show that, compared with the conventional multi-variable variable-length algorithm, the privacy protection method proposed in this paper has improved the privacy protection level of related devices, and the information distortion has been properly resolved. The construction of the release model can prevent susceptible data with identity tags from being tampered with, stolen, and leaked by criminals. It can avoid causing great spiritual and property losses to individuals, and avoid harming public safety caused by information leakage.

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>

Blockchain-based Data Integrity and Privacy Protection Method for Swarm Robotics

2021 ◽  
pp. 698-707
Author(s):  
Bangyin Li ◽  
Yutao Chen ◽  
Zhiqiang Zuo ◽  
Jie Huang
Keyword(s):  
Privacy Protection ◽  
Swarm Robotics ◽  
Data Integrity ◽  
Protection Method
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