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An Enhanced Middleware for Collaborative Privacy in IPTV Recommender Services [article]

Ahmed M. Elmisery, Dmitri Botvich
2017 arXiv   pre-print
In this work, we introduce a framework for private recommender service based on Enhanced Middleware for Collaborative Privacy (EMCP).  ...  We utilize trust mechanism to augment the accuracy and privacy of the recommendations. Trust heuristic spot users who are trustworthy with respect to the user requesting the recommendation.  ...  ACKNOWLEDGMENT This work has received support from the Higher Education Authority in Ireland under the PRTLI Cycle 4 programme, in the FutureComm project (Serving Society: Management of Future Communications  ... 
arXiv:1711.07593v1 fatcat:36mj4fysbnelrlxm63ko7af36q

Preserving privacy in collaborative filtering through distributed aggregation of offline profiles

Reza Shokri, Pedram Pedarsani, George Theodorakopoulos, Jean-Pierre Hubaux
2009 Proceedings of the third ACM conference on Recommender systems - RecSys '09  
In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy.  ...  Applying our method to the Netflix prize dataset, we show the effectiveness of the algorithm in solving the tradeoff between privacy and accuracy in recommender systems in an applicable way.  ...  CONCLUSION AND FUTURE WORK In this work, we proposed a novel method for privacy preservation in collaborative filtering recommendation systems.  ... 
doi:10.1145/1639714.1639741 dblp:conf/recsys/ShokriPTH09 fatcat:ynrbbqjo7jbg3fi7v3t6343iv4

Holistic Collaborative Privacy Framework for Users' Privacy in Social Recommender Service [article]

Ahmed M. Elmisery, Seungmin Rho, Dmitri Botvich
2014 pre-print
We proposed a collaborative privacy middleware that executes a two stage concealment process within a distributed data collection protocol in order to attain this claim.  ...  In this paper, we assert that utilizing third-party recommender services to generate accurate referrals are feasible, while preserving the privacy of the users' sensitive information which will be residing  ...  We realized that there would be many challenges in building a collaborative privacy framework for social recommender services.  ... 
doi:10.13140/2.1.1714.5289 arXiv:1411.3737v1 fatcat:2x5fho3hhvcmzmu332e7b3fib4

Data Privacy Preservation in Collaborative Filtering Based Recommender Systems

Xiwei Wang
2015 unpublished
In this chapter, a privacy-preserving data update scheme is proposed for collaborative filtering based recommender systems.  ...  This is the most fundamental privacy problem in collaborative filtering. Thus privacy-preserving collaborative filtering algorithms [12, 59, 53] were proposed to resolve the problem.  ... 

Privacy Concerns when Modeling Users in Collaborative Filtering Recommender Systems [chapter]

Sylvain Castagnos
Social and Human Elements of Information Security  
This chapter investigates ways to deal with privacy rules when modeling preferences of users in recommender systems based on collaborative filtering.  ...  The authors hope that their attempts to provide an unified vision of privacy rules through the related works and a generic privacy-enhancing procedure will help researchers and practitioners to better  ...  This chapter focuses mainly on privacy aspects in recommenders based on collaborative filtering algorithms. First we will provide an overview about privacy issues.  ... 
doi:10.4018/978-1-60566-036-3.ch014 fatcat:ozubna37ijcxbm6cxeu7r62hfm

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

Liang Xiao, Qibei Lu, Feipeng Guo
2020 Sustainability  
In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant  ...  Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information.  ...  Li et al. studied an individual privacy policy mechanism based on cluster recommendation, analyzed the privacy concerns in the user-based collaborative filtering recommendation process, and proposed a  ... 
doi:10.3390/su12073036 fatcat:5tpnbgk6rzet5dhix5bsx3dbgi

Privacy-preserving Collaborative Filtering based on Randomized Perturbation Techniques and Secure Multiparty Computation

Songjie Gong
2011 International Journal of Advancements in Computing Technology  
To reserve privacy in collaborative filtering recommender systems, this paper presented a collaborative filtering algorithm based on randomized perturbation techniques and secure multiparty computation  ...  With the evolution of the Internet, collaborative filtering techniques are becoming increasingly popular in E-commerce recommender systems.  ...  In this paper, we presented a collaborative filtering algorithm based on randomized perturbation techniques and secure multiparty computation to reserve privacy in collaborative filtering recommender systems  ... 
doi:10.4156/ijact.vol3.issue4.10 fatcat:vvhjn2fxivem7lcjumggxye3mi

A Method of Personalized Recommendation Based on Differential Privacy

2017 DEStech Transactions on Computer Science and Engineering  
In this paper, a personalized recommendation method based on differential privacy preserving (DPP) is proposed.  ...  Traditional recommendation system built on a collaborative filtering approach; it is difficult to provide a strict privacy protection.  ...  McSsherry et al. applied the differential privacy in the collaborative filtering recommendation system, which described the differential privacy treatment for the item-to-item covariance matrix, and expanded  ... 
doi:10.12783/dtcse/cst2017/12561 fatcat:j32r33j4crcoheqldf6toe5iza

Privacy-preserving collaborative recommendations based on random perturbations

Nikolaos Polatidis, Christos K. Georgiadis, Elias Pimenidis, Haralambos Mouratidis
2017 Expert systems with applications  
In the case of centralized approach, there are a number of different methods for privacy-preserving recommendations: A classical approach for privacy-preserving collaborative filtering is that of rating  ...  For example, collaborative filtering can be used as a part of a system that is used to provide privacy-preserving location-based services in mobile recommender systems.  ... 
doi:10.1016/j.eswa.2016.11.018 fatcat:syehrczbzngjdnzmfvfxumtxiq

Differentially Private User-based Collaborative Filtering Recommendation Based on K-means Clustering [article]

Zhili Chen, Yu Wang, Shun Zhang, Hong Zhong, Lin Chen
2018 arXiv   pre-print
Recently, the notion of differential privacy (DP) has been applied to privacy preservation for collaborative filtering recommendation algorithms.  ...  In this paper, in order to address the performance degradation problem, we propose a differentially private user-based collaborative filtering recommendation scheme based on k-means clustering (KDPCF).  ...  , further improving the recommendation performance under the same privacy levels.  ... 
arXiv:1812.01782v1 fatcat:djgksyzfnvdllh74jwydcwtj3e

Mobile recommender systems: Identifying the major concepts

Elias Pimenidis, Nikolaos Polatidis, Haralambos Mouratidis
2018 Journal of information science  
Keywords Mobile recommender systems, Collaborative filtering, Context, Privacy Recommender systems, depending on the method they employ can be classified in one of the following categories (Bobadilla et  ...  The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved.  ...  Privacy is an important issue in recommender systems and most techniques are either based on collaborative filtering rating privacy or location privacy.  ... 
doi:10.1177/0165551518792213 fatcat:rr6o7ewkvvalvj5qkbdvz3qmua

Privacy in Recommender Systems [chapter]

Arjan J. P. Jeckmans, Michael Beye, Zekeriya Erkin, Pieter Hartel, Reginald L. Lagendijk, Qiang Tang
2012 Computer Communications and Networks  
This chapter aims to provide insight into privacy in recommender systems. First, we discuss different types of existing recommender systems.  ...  Second, we give an overview of the data that is used in recommender systems. Third, we examine the associated risks to data privacy.  ...  In this section, we will look into privacy in recommender systems, and potential privacy concerns with a focus on user privacy.  ... 
doi:10.1007/978-1-4471-4555-4_12 dblp:series/ccn/JeckmansBEHLT13 fatcat:leia3xo3ovfo3h5ph5tmphr4qi

Mobile recommender systems: Identifying the major concepts [article]

Elias Pimenidis, Nikolaos Polatidis, Haralambos Mouratidis
2018 arXiv   pre-print
The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved.  ...  This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain.  ...  Privacy is an important issue in recommender systems and most techniques are either based on collaborative filtering rating privacy or location privacy.  ... 
arXiv:1805.02276v1 fatcat:fwfb6cnchnaz7lave2oqb7zsue

H2E: A Privacy Provisioning Framework for Collaborative Filtering Recommender System

Muhammad Usman Ashraf, Department of Computer Science, GC Women University, Sialkot, Pakistan, Mubeen Naeem, Amara Javed, Iqra Ilyas
2019 International Journal of Modern Education and Computer Science  
Today, abundant recommender systems have been developed for different fields and we put an effort on collaborative filtering (CF) recommender system.  ...  Furthermore, in order to evaluate the privacy level, H2E was implementing in medicine recommender system and compared the consequences with existing state-of-the-art privacy protection mechanisms.  ...  RELATED WORK Several state-of-the-art methods have been proposed that are utilized in a collaborative filtering recommender system framework for provisioning privacy to end user.  ... 
doi:10.5815/ijmecs.2019.09.01 fatcat:puzoxonekrh4pjcwkliwhbfpym

An algorithm for efficient privacy-preserving item-based collaborative filtering

Dongsheng Li, Chao Chen, Qin Lv, Li Shang, Yingying Zhao, Tun Lu, Ning Gu
2016 Future generations computer systems  
In this paper, an efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation  ...  However, privacy issue arises in this process as sensitive user private data are collected by the recommender server.  ...  Meanwhile, the protocol is privacy-preserving in the semi-honest model [22] , in which all parties Privacy-Preserving Item-based Collaborative Filtering Using SMPC Item-based collaborative filtering  ... 
doi:10.1016/j.future.2014.11.003 fatcat:2anur3y3kbfcvbdphkiax3adsy
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