scholarly journals Client Side Privacy Protection Using Personalized Web Search

2016 ◽  
Vol 79 ◽  
pp. 1029-1035 ◽  
Author(s):  
Sharvari V. Malthankar ◽  
Shilpa Kolte
Keyword(s):  
Privacy Protection ◽  
Web Search ◽  
Client Side

scholarly journals Location Privacy Protection Scheme for LBS in IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hongtao Li ◽  
Xingsi Xue ◽  
Zhiying Li ◽  
Long Li ◽  
Jinbo Xiong
Keyword(s):  
Voronoi Diagram ◽  
Privacy Protection ◽  
Location Privacy ◽  
Location Information ◽  
Protection Scheme ◽  
Server Side ◽  
Location Data ◽  
Location Privacy Protection ◽  
Client Side ◽  
Grid Division

The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

Kronecker product and bat algorithm-based coefficient generation for privacy protection on cloud

2017 ◽  
Vol 08 (03) ◽  
pp. 1750021 ◽  
Author(s):  
Nandkishor P. Karlekar ◽  
N. Gomathi
Keyword(s):  
Privacy Protection ◽  
Kronecker Product ◽  
Privacy Preservation ◽  
Bat Algorithm ◽  
Original Data ◽  
Data Publishing ◽  
Sensitive Data ◽  
Cloud Data ◽  
Preservation Method ◽  
Client Side

Due to widespread growth of cloud technology, virtual server accomplished in cloud platform may collect useful data from a client and then jointly disclose the client’s sensitive data without permission. Hence, from the perspective of cloud clients, it is very important to take confident technical actions to defend their privacy at client side. Accordingly, different privacy protection techniques have been presented in the literature for safeguarding the original data. This paper presents a technique for privacy preservation of cloud data using Kronecker product and Bat algorithm-based coefficient generation. Overall, the proposed privacy preservation method is performed using two important steps. In the first step, PU coefficient is optimally found out using PUBAT algorithm with new objective function. In the second step, input data and PU coefficient is then utilized for finding the privacy protected data for further data publishing in cloud environment. For the performance analysis, the experimentation is performed with three datasets namely, Cleveland, Switzerland and Hungarian and evaluation is performed using accuracy and DBDR. From the outcome, the proposed algorithm obtained the accuracy of 94.28% but the existing algorithm obtained only the 83.64% to prove the utility. On the other hand, the proposed algorithm obtained DBDR of 35.28% but the existing algorithm obtained only 12.89% to prove the privacy measure.

scholarly journals Designing Improvisation in Privacy Protection for Personalized Web Search using deep learning approach

2021 ◽  
Vol 10 (6) ◽  
pp. 25336-25346
Author(s):  
Ms. Sushama K Bhandare ◽  
Dr. Avinash S. Kapse
Keyword(s):  
Private Information ◽  
Privacy Protection ◽  
Web Search ◽  
Greedy Algorithms ◽  
User Preferences ◽  
Privacy Risk ◽  
Major Barrier ◽  
Index Terms ◽  
Prediction Mechanism

Abstract—Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users’ reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. We study privacy protection in PWS applications that model user preferences as hierarchical user profiles. We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user- specified privacy requirements. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. We present two greedy algorithms, namely GreedyDP and GreedyIL, for runtime generalization. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. Extensive experiments demonstrate the effectiveness of our framework. The experimental results also reveal that GreedyIL significantly outperforms GreedyDP in terms of efficiency.   Index Terms—Privacy protection, personalized web search, utility, risk, profile

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