Collaborative Filtering on the Blockchain: A Secure Recommender System for e-Commerce

In collaborative filtering approaches, recommendations are inferred from user data. A large volume and a high data quality is essential for an accurate and precise recommender system. As consequence, companies are collecting large amounts of personal user data. Such data is often highly sensitive and ignoring users’ privacy concerns is no option. Companies address these concerns with several risk reduction strategies, but none of them is able to guarantee cryptographic secureness. To close that gap, the present paper proposes a novel recommender system using the advantages of blockchain-supported secure multiparty computation. A potential costumer is able to allow a company to apply a recommendation algorithm without disclosing her personal data. Expected benefits are a reduction of fraud and misuse and a higher willingness to share personal data. An outlined experiment will compare users’ privacy-related behavior in the proposed recommender system with existent solutions.

[1]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[2]  H. Jeff Smith,et al.  Information Privacy: Measuring Individuals' Concerns About Organizational Practices , 1996, MIS Q..

[3]  Alex Pentland,et al.  Decentralizing Privacy: Using Blockchain to Protect Personal Data , 2015, 2015 IEEE Security and Privacy Workshops.

[4]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[5]  Judy Drennan,et al.  Privacy, Risk Perception, and Expert Online Behavior: An Exploratory Study of Household End Users , 2006, J. Organ. End User Comput..

[6]  Annie I. Antón,et al.  Examining Internet privacy policies within the context of user privacy values , 2005, IEEE Transactions on Engineering Management.

[7]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[8]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[9]  Ashok Kumar,et al.  From mass customization to mass personalization: a strategic transformation , 2007 .

[10]  Vikas Sindhwani,et al.  Recommender Systems , 2017, Encyclopedia of Machine Learning and Data Mining.

[11]  George Roussos,et al.  Consumer perceptions of privacy, security and trust in ubiquitous commerce , 2004, Personal and Ubiquitous Computing.

[12]  Hamid R. Nemati,et al.  The Effect of Consumer Privacy Empowerment on Trust and Privacy Concerns in E-Commerce , 2007, Electron. Mark..

[13]  Mayuram S. Krishnan,et al.  The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization , 2006, MIS Q..

[14]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[15]  Jemal H. Abawajy,et al.  Privacy models for big data: a survey , 2015, Int. J. Big Data Intell..

[16]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[17]  Peng-Ting Chen,et al.  Personalized mobile advertising: Its key attributes, trends, and social impact , 2012 .

[18]  Alex Pentland,et al.  Enigma: Decentralized Computation Platform with Guaranteed Privacy , 2015, ArXiv.

[19]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[20]  M. Wedel,et al.  The Effectiveness of Customized Promotions in Online and Offline Stores , 2009 .