scholarly journals Granulation as a Privacy Protection Mechanism

2007 ◽  
pp. 256-273
Author(s):  
Da-Wei Wang ◽  
Churn-Jung Liau ◽  
Tsan-sheng Hsu
Keyword(s):  
Privacy Protection ◽  
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.

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.

Privacy Protection Mechanism in Grid Computing Environment

EC2ND 2005 ◽  
2007 ◽  
pp. 33-39 ◽  
Author(s):  
MingChu Li ◽  
Hongyan Yao ◽  
Cheng Guo ◽  
Na Zhang
Keyword(s):  
Grid Computing ◽  
Privacy Protection ◽  
Computing Environment ◽  
Protection Mechanism

scholarly journals Cross-Platform Strong Privacy Protection Mechanism for Review Publication

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Mingzhen Li ◽  
Yunfeng Wang ◽  
Yang Xin ◽  
Hongliang Zhu ◽  
Qifeng Tang ◽  
...  
Keyword(s):  
Statistical Inference ◽  
Privacy Protection ◽  
Service System ◽  
Review System ◽  
User Privacy ◽  
Protection Mechanism ◽  
Cross Platform ◽  
Inference Attack

As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

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
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