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Recommender systems have a varied history concluding into modern day where more sophisticated automated integrated computer cloud based suggestion and analysis systems exist. As recommender systems become more contextual and deeper in learning analysis based on the data the systems, pulls there arises more confidentiality, privacy, accessibility, and security issues regarding end user’s data. Key identifying information from this data can be information that are confidential and of security in nature. Data information from movie preferences on Netflix for example via social media sites like Facebook can lead to both demographic data sharing to acquaintances and strangers as well as increase the risk for data hacking. Furthermore, social media sites are in part based upon recommender systems and not search engine technologies. A true recommender system filters even greater information than social media sites alone. There is a significant difference between a system that recommends choices for you and a system that does not. Google.com is a great example that based on your search and who you are the result of the data differs. This research paper will examine key confidentiality, privacy, accessibility, and security issues within the context of recommender systems with several key examples. The researcher’s use of Netflix as the key model for this aforementioned examination and analysis are combined with other relevant examples and are also investigated. A recent encryption method, homomorphic encryption is proposed and discussed.

CONFIDENTIALITY, PRIVACY, ACCESSIBILITY AND SECURITY OF BIG DATA USAGES WITHIN MOBILE RECOMMENDER SYSTEMS, AS SOCIETY EMBRACES CLOUD BASED TECHNOLOGY