Section: Overall Objectives

Highly Personalized On-Line Services

Ever increasing volumes of data are being produced and made available from a growing number of sources (Internet of Things sensors, open data repositories, user-generated content services). As a result, digital users find it increasingly difficult to face the data deluge they are subjected to without additional help. This difficulty has fueled the rise of notification solutions over traditional search, in order to push few but relevant information items to users rather than leave them to sieve through a large mass of non-curated data. To provide such personalized services, most companies rely today on centralized or tightly coupled systems hosted in data centers or in the cloud. These systems use advanced data-mining and machine learning techniques to deliver enhanced, personalized, services to users and companies, and often exploit highly parallelized data analytics frameworks such as Spark, and Flink.

Selecting the best information for a user in order to provide a personalized experience requires however to gather enough information about this user, which raises a number of important technical challenges and privacy protection issues. More precisely, this concentration poses strong risks to the privacy of users, and limits the scope of personalization to tightly integrated datasets. The use of large monolithic infrastructures also limits the use of machine learning and personalization to situations in which data is fully available to the organization managing the underlying computing infrastructure. This set-up prevents for instance cases in which sensitive data may not be shared freely, but might be of mutual interest to several independent participants in order to construct common machine learning models usable by all. Such situations occur for instance in the context of the mining of health-records by independent health-organizations, or in the collective harnessing of individual on-line profiles for personalization purpose by private users.

Alternative decentralized approaches that eschew the need for a central all-encompassing authority holds the promise of delivering knowledge while protecting individual participants. Constructing such systems requires however to address the inherent tension between the need to limit sensitive individual leaks, while maximizing collectively gained insights. Answering this tension calls on techniques and approaches from distributed systems, information theory, security, and randomized processes, making it a rich and dense research area, with a high impact potential. The problem of distributed privacy in a digital interconnected age further touches on interdisciplinary questions of Law, Sociology and Public Policy, which we think can only be explored in collaboration with colleagues from these fields.