Survey and Analysis of Cryptographic Techniques for Privacy Protection in Recommender Systems

2018 ◽  
pp. 691-706
Author(s):  
Taiwo Blessing Ogunseyi ◽  
Cheng Yang
Keyword(s):  
Recommender Systems ◽  
Privacy Protection ◽  
Cryptographic Techniques

scholarly journals A Survey of Collaborative Filtering Techniques

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaoyuan Su ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Privacy Protection ◽  
State Of The Art ◽  
Predictive Performance ◽  
The State ◽  
Data Sparsity ◽  
Research And Practice ◽  
Model Based ◽  
Comprehensive Survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

scholarly journals Privacy-preserving recommendations in context-aware mobile environments

2017 ◽  
Vol 25 (1) ◽  
pp. 62-79 ◽  
Author(s):  
Nikolaos Polatidis ◽  
Christos K. Georgiadis ◽  
Elias Pimenidis ◽  
Emmanouil Stiakakis
Keyword(s):  
Recommender Systems ◽  
Privacy Protection ◽  
Personal Information ◽  
Contextual Information ◽  
Context Aware ◽  
User Privacy ◽  
Data Set ◽  
Content Type ◽  
Privacy Concerns ◽  
Usability Tests

Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems

2011 ◽  
pp. 247-260
Author(s):  
Sylvain Castagnos
Keyword(s):  
Information Systems ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Privacy Protection ◽  
Personal Data ◽  
Distributed Architectures ◽  
Privacy Concerns ◽  
Good Compromise ◽  
Privacy Laws

This chapter investigates ways to deal with privacy rules when modeling preferences of users in recommender systems based on collaborative filtering. It argues that it is possible to find a good compromise between quality of predictions and protection of personal data. Thus, it proposes a methodology that fulfills with strictest privacy laws for both centralized and distributed architectures. The authors hope that their attempts to provide a unified vision of privacy rules through the related works and a generic privacy-enhancing procedure will help researchers and practitioners to better take into account the ethical and juridical constraints as regards privacy protection when designing information systems.

A Decentralized Security Framework for Web-Based Social Networks

2011 ◽  
pp. 356-387
Author(s):  
Barbara Carminati ◽  
Elena Ferrari ◽  
Andrea Perego
Keyword(s):  
Social Networks ◽  
Web Sites ◽  
Privacy Protection ◽  
Personal Information ◽  
Sensitive Information ◽  
Security Framework ◽  
Web Based ◽  
Social Network Data ◽  
Protection Mechanisms ◽  
Cryptographic Techniques

The wide diffusion and usage of social networking Web sites in the last years have made publicly available a huge amount of possible sensitive information, which can be used by third-parties with purposes different from the ones of the owners of such information. Currently, this issue has been addressed by enforcing into Web-based Social Networks (WBSNs) very simple protection mechanisms, or by using anonymization techniques, thanks to which it is possible to hide the identity of WBSN members while performing analysis on social network data. However, we believe that further solutions are needed, to allow WBSN members themselves to decide who can access their personal information and resources. To cope with this issue, in this chapter we illustrate a decentralized security framework for WBSNs, which provide both access control and privacy protection mechanisms. In our system, WBSN members can denote who is authorized to access the resources they publish and the relationships they participate in, in terms of the type, depth, and trust level of the relationships existing between members of a WBSN. Cryptographic techniques are then used to provide a controlled sharing of resources while preserving relationship privacy.

scholarly journals Differential Privacy and Bayesian for Context-Aware Recommender Systems

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Bayesian Network ◽  
Recommender Systems ◽  
Privacy Protection ◽  
Recommendation System ◽  
Differential Privacy ◽  
Contextual Information ◽  
Network Technology ◽  
Context Aware ◽  
Individual Privacy ◽  
Network Technologies

Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.

Proposal Tightens Privacy Protection

2010 ◽  
Vol 43 (13) ◽  
pp. 77
Author(s):  
MARY ELLEN SCHNEIDER
Keyword(s):  
Privacy Protection
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