Generating Private Recommendations Efficiently Using GAE Datastore and Data Packing

Abstract: People use social networks to get in touch with other people, and create and share content that includes personal information, images, and videos. The service providers have access to the content provided by their users and have the right to process collected data and distribute them to third parties. A very common service provided in social networks is to generate recommendations for finding new friends, groups, and events using collaborative filtering techniques. The data required or the collaborative filtering algorithm is collected from various resources including users‘ profiles and behaviors. Online shopping services increase the likelihood of a purchase by providing personalized suggestions to their customers. To find services and products suitable to a particular customer, the service provider processes collected user data like user preferences and click logs. In all of the above services and in many others, recommender systems based on collaborative filtering techniques that collect and process personal user data constitute an essential part of the service. On one hand, people benefit from online services. On the other hand, direct access to private data by the service provider has potential privacy risks for the users since the data can be processed for other purposes, transferred to third parties without user consent, or even stolen. Recent studies show that the privacy considerations in online services seem to be one of the most important factors that threaten the healthy growth of e- business. Therefore, it is important to protect the privacy of the users of online services for the benefit of both individuals and business. Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties, but not the service provider. This creates a serious privacy risk for the users. This paper aims to protect the private data against the service provider while preserving the functionality of the system. We used GAE Datastore for the processing of private data to generate private recommendations by introducing semi-trusted third party and using data packing.

Keywords: GAE Datastore, Privacy, Recommender Systems, Data Packing, Collaborative Filtering, Private Recommendations