Recommender systems are a subclass of information filtering system that seeks to predict the rating that a user would give to an item that he has not yet considered, using a model built from the characteristics of items and/or users. Nowadays, recommender systems play an important role in highly rated commercial websites such as Amazon and Netflix. Besides, recommender systems or personalization techniques at large have been adopted in a wide range of applications, from entertainment to precision medicine.
In order to compute recommendations for users, a recommendation service provider needs to collect a lot of personal data from its customers, such as ratings, transaction history, and location. This makes recommender systems a double-edged sword. On one side users get better recommendations when they reveal more personal data, but on the flip side they sacrifice more privacy if they do so. With the ever stricter privacy regulations such as GDPR, how to benefit from aggregated personal data while complying to the laws has become an urgent issue for the researchers and the whole society.
BRAIDS is designed to study the utility-privacy dilemma. The utility means the quality of recommendations received by the end users, while privacy means the information disclosure to the service provider and the users involved in the underlying recommender system.
The project team had the ambition to protect users’ privacy to the maximal extent while still enabling them to receive very accurate/satisfactory recommendations. To this end, they have planned to investigate the realistic privacy notions for recommender systems, and invent privacy-enhancing technologies that allow recommendations to be generated in a privacy-preserving manner (e.g. generated on encrypted data).
During the research, BRAIDS researchers have identified two major findings, which not only apply to recommender systems but also may apply to machine learning and data mining in general:
Besides theoretical results, BRAIDS researchers have also planned to closely study the performances of the proposed system and perform some case studies. Even though this project focuses on recommender systems, they have expected the resulting technologies (e.g. building blocks) can be applied to other related services.
Moreover, they wish to figure out the interaction of privacy issues and other issues recommender system, and to lay the knowledge background for further research projects.
The research paper, Differentially Private Neighborhood-Based Recommender Systems, by Jun Wang and Qiang Tang, has been awarded the Best Student Paper award by the SEC 2017 conference (i.e. ICT Systems Security and Privacy Protection - 32nd IFIP TC 11 International Conference).
More information on http://www.tangqiang.eu/braids.htm