Rank Computation by a Recommender System: Malicious Attacks, Secret Sharing, Privacy, Fairness, Correctness & Rationality Sumit Chakraborty Fellow, Management Information Systems (Indian Institute of Management Calcutta), BEE (Jadavpur University), India E-mail:
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[email protected]; Phone: 91-9940433441 Abstract : This work deals with the problem of rank computation by a corrupted recommender system. It presents Fair Recommendation Algorithm (FRA) and related complexity analysis. Secure multi-party computation may be an interesting solution for the aforesaid problem from the perspectives of secret sharing, privacy, fairness, correctness, rationality, trust, commitment, integrity, consistency, transparency and accountability. It is also important to verify authentication, authorization, correct identification, privacy and audit of rank computation by an efficient recommender system. Another critical issue is how to share a secret through threshold cryptographic schema. This work analyzes two test cases with the support of fair recommendation algorithm: (a) ranking in assessment and accreditation of education institutes and also digital advertising and (b) rank computation in joint entrance examination (e.g. medical, engineering). This study can be extended to various application domains such as financial service, healthcare, education and corporate governance. Keywords: Recommender System, Shilling attack, Rank computation, Secret Sharing, Privacy, Fairness, Correctness, Rationality, Secure multi-party Computation, Threshold cryptography. 1. INTRODUCTION Traditionally, a Recommender System is an information system giving suggestions for specific set of items in electronic commerce and mobile commerce applications [1]. The suggestions are used in purchasing decision making processes such as what items to buy, what books or online news to read, what songs or music to listen or what movies to watch. An item is an object what the information system recommends to the users. A recommender system is designed with graphical user interface, specific items and core recommendation algorithms to identify or predict a set of useful items for the users or customers or clients or service consumers [2]. The system tries to predict the utility or compa