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Achieving Optimal Privacy in Trust-Aware Social Recommender Systems
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.ORCID iD: 0000-0002-4722-0823
2010 (English)In: SOCIAL INFORMATICS / [ed] Bolc L; Makowski M; Wierzbicki A, 2010, Vol. 6430, 62-79 p.Conference paper, Published paper (Refereed)
Abstract [en]

Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommenders accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.

Place, publisher, year, edition, pages
2010. Vol. 6430, 62-79 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 6430
Keyword [en]
Privacy, Trust, Optimization, Data Disguising, Social networks, Collaborative filtering, Recommender systems
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-32635DOI: 10.1007/978-3-642-16567-2_5ISI: 000289030500005Scopus ID: 2-s2.0-78449282757ISBN: 978-3-642-16566-5 (print)OAI: oai:DiVA.org:kth-32635DiVA: diva2:411502
Conference
2nd International Conference on Social Informatics, Laxenburg, AUSTRIA, OCT 27-29, 2010
Note

QC 20110418

Available from: 2011-04-18 Created: 2011-04-18 Last updated: 2013-02-20Bibliographically approved
In thesis
1. Trust-Based User Profiling
Open this publication in new window or tab >>Trust-Based User Profiling
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

We have introduced the notion of user profiling with trust, as a solution to theproblem of uncertainty and unmanageable exposure of personal data duringaccess, retrieval and consumption by web applications. Our solution sug-gests explicit modeling of trust and embedding trust metrics and mechanismswithin very fabric of user profiles. This has in turn allowed information sys-tems to consume and understand this extra knowledge in order to improveinteraction and collaboration among individuals and system. When formaliz-ing such profiles, another challenge is to realize increasingly important notionof privacy preferences of users. Thus, the profiles are designed in a way toincorporate preferences of users allowing target systems to understand pri-vacy concerns of users during their interaction. A majority of contributionsof this work had impact on profiling and recommendation in digital librariescontext, and was implemented in the framework of EU FP7 Smartmuseumproject. Highlighted results start from modeling of adaptive user profilesincorporating users taste, trust and privacy preferences. This in turn led toproposal of several ontologies for user and content characteristics modeling forimproving indexing and retrieval of user content and profiles across the plat-form. Sparsity and uncertainty of profiles were studied through frameworksof data mining and machine learning of profile data taken from on-line so-cial networks. Results of mining and population of data from social networksalong with profile data increased the accuracy of intelligent suggestions madeby system to improving navigation of users in on-line and off-line museum in-terfaces. We also introduced several trust-based recommendation techniquesand frameworks capable of mining implicit and explicit trust across ratingsnetworks taken from social and opinion web. Resulting recommendation al-gorithms have shown to increase accuracy of profiles, through incorporationof knowledge of items and users and diffusing them along the trust networks.At the same time focusing on automated distributed management of profiles,we showed that coverage of system can be increased effectively, surpassingcomparable state of art techniques. We have clearly shown that trust clearlyelevates accuracy of suggestions predicted by system. To assure overall pri-vacy of such value-laden systems, privacy was given a direct focus when archi-tectures and metrics were proposed and shown that a joint optimal setting foraccuracy and perturbation techniques can maintain accurate output. Finally,focusing on hybrid models of web data and recommendations motivated usto study impact of trust in the context of topic-driven recommendation insocial and opinion media, which in turn helped us to show that leveragingcontent-driven and tie-strength networks can improve systems accuracy forseveral important web computing tasks.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. xi, 48 p.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:10
Keyword
trust, userprofiling, userprofiles, privacy, interest, socialnetwork, recommendersystems
National Category
Information Systems
Identifiers
urn:nbn:se:kth:diva-118488 (URN)978-91-7501-651-1 (ISBN)
Public defence
2013-03-08, C1 Sal, Electrum, ICT/KTH, Isafjordsgatan 20, Kista, 13:00 (English)
Opponent
Supervisors
Note

QC 20130219

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2014-01-24Bibliographically approved

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Matskin, Mihhail

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