Privacy in Retrieval, Computing, and Learning

Sennur Ulukus, Salman Avestimehr, Michael Gastpar, Syed Ali Jafar, Ravi Tandon, Chao Tian

Abstract: The increasing prevalence of massive datasets makes the outsourcing of storage and computation tasks to distributed servers a necessity. This raises a number of concerns regarding the security and integrity of stored information, the privacy of accessing desired information, the communication overhead of distributed systems, the latency, reliability, and complexity of distributed computing, and privacy in distributed training and learning systems. Recent breakthroughs from coding, communication, and information-theoretic perspectives have opened up exciting new research avenues for these topics. There are many theoretical and practical open problems. This Special Issue is dedicated to communication theory, coding theory, information theory, signal processing, and networking aspects of privacy in information retrieval, privacy in coded computing over distributed servers, and privacy in distributed learning.