LTPPM: a location and trajectory privacy protection mechanism in participatory sensing

2012 ◽  
Vol 15 (1) ◽  
pp. 155-169 ◽  
Author(s):  
Sheng Gao ◽  
Jianfeng Ma ◽  
Weisong Shi ◽  
Guoxing Zhan
Keyword(s):  
Privacy Protection ◽  
Participatory Sensing ◽  
Protection Mechanism

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

2021 ◽  
Author(s):  
Zice Sun ◽  
Yingjie Wang ◽  
Zhipeng Cai ◽  
Tianen Liu ◽  
Xiangrong Tong ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Two Stage ◽  
Mobile Crowdsourcing ◽  
Protection Mechanism

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhengqiang Ge ◽  
Xinyu Liu ◽  
Qiang Li ◽  
Yu Li ◽  
Dong Guo
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Short Term ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Term Memory ◽  
Protection Mechanism ◽  
Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

QLDS: A Novel Design Scheme for Trajectory Privacy Protection with Utility Guarantee in Participatory Sensing

2018 ◽  
Vol 17 (6) ◽  
pp. 1397-1410 ◽  
Author(s):  
Zhe Xiao ◽  
Jing-Jing Yang ◽  
Ming Huang ◽  
Loganathan Ponnambalam ◽  
Xiuju Fu ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Participatory Sensing ◽  
Design Scheme ◽  
Novel Design

A Privacy Protection Mechanism for NoSql Database Based on Data Chunks

2016 ◽  
Author(s):  
Shibin Sun ◽  
Yuliang Shi ◽  
Shidong Zhang ◽  
Lizhen Cui
Keyword(s):  
Privacy Protection ◽  
Protection Mechanism ◽  
Nosql Database

scholarly journals mPrivacy: A Privacy Policy Engine and Safeguard Mechanism in Mobile Devices

2021 ◽  
Vol 11 (24) ◽  
pp. 11629
Author(s):  
Zhong Zhang ◽  
Minho Shin
Keyword(s):  
Mobile Devices ◽  
Private Information ◽  
Privacy Protection ◽  
Information Quality ◽  
Privacy Policy ◽  
Protection Mechanism ◽  
New Information ◽  
Inference Algorithms ◽  
Protection Mechanisms ◽  
Safeguard Mechanism

Within the scope of mobile privacy, there are many attack methods that can leak users’ private information. The communication between applications can be used to violate permissions and access private information without asking for the user’s authorization. Hence, many researchers made protection mechanisms against privilege escalation. However, attackers can further utilize inference algorithms to derive new information out of available data or improve the information quality without violating privilege limits. In this work. we describe the notion of Information Escalation Attack and propose a detection and protection mechanism using Inference Graph and Policy Engine for the user to control their policy on the App’s privilege in information escalation. Our implementation results show that the proposed privacy protection service is feasible and provides good useability.

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