The recent data revolution is driving many aspects of modern societal and economic progress. Most of this massive data is now stored in the cloud to enable easy access for a myriad of users who wish to share information including, for example, photos, videos, publications, opinions, and scientific data. Unfortunately, this has come at the expense of the user's privacy whose online activity can be used to profile him/her, making large parts of the population an easy target for discrimination and possible persecution. This research aims at addressing the privacy challenge of data in the cloud by focusing on the problem of Private Information Retrieval (PIR) and Search in distributed storage systems (DSSs). PIR schemes enable users to query data without revealing information about the queries and hence their personal preferences, tendencies, health, or other traits.
Classical information theoretic PIR schemes require data to be replicated, which is not a scalable solution given the exponential growth of data. This research aims at creating a unified framework for studying coding schemes that, in addition to providing data reliability, cater to the need of private queries. The focus of the proposed research is on (i) explicit constructions of codes and PIR schemes that address practical and important aspects of distributed storage, such as storage cost, network communication cost, disk reads, latency and computations; (ii) explicit constructions of codes and schemes for private keyword search; (iii) characterization of the fundamental limits and tradeoffs between reliability, privacy and the different system overheads; (iv) testing software implementations of the schemes on real genomic and social science data. The project also incorporates several educational and outreach efforts, including the development of new publicly accessible online content on information theory, security, and privacy in distributed storage systems as well as pre-college outreach through the Global Leaders Program at the PI's institution.
|Effective start/end date
|9/1/17 → 2/28/23
- National Science Foundation: $639,961.00