CIF

The project aims at quantifying a general network's inner potential for supporting various forms of security by achieving secret common randomness between pairs or groups of its nodes. Statistical and computational secrecy measures are being considered against a general passive adversary. Common-randomness-achieving protocols are classified into two groups: culture-building and crowd-shielding. The former achieves common randomness between nodes situated in close proximity of each other, from correlated observations of specific (natural or induced) network phenomena.

Common shared keys, which can be used for encryption/decryption, authentication and various other security primitives, plays a fundamental role in securing modern digital systems. Due to such significant importance, there has been a tremendous amount of work on the design of key generation/distribution schemes. Despite of these efforts, significant challenges, such as scalability and availability, remain to be fully addressed. This project investigates information theoretic approaches for secret key generation with focuses on fundamental and challenging issues under more realistic models.

As communication networks play an increasingly vital role in our society, ensuring the confidentiality of information transmission and storage has become an increasing concern. New classes of networks, such as heterogeneous wireless networks and distributed storage networks, are emerging, in which the deployment of off-the-shelf cryptographic solutions faces several limitations.

In today's communication networks, most data flow through wireless links which are particularly vulnerable to eavesdropping attacks. While currently available cryptographic algorithms provide useful protection against computationally limited eavesdroppers, the ever-increasing computational power of adversaries necessitates techniques that provide stronger forms of security guarantees. Information-theoretic physical layer security provides unconditional security guarantees that are valid even against computationally unlimited adversaries.

Privacy is a fundamental individual right. In the era of big data, large amounts of data about individuals are collected both voluntarily (e.g., frequent flier/shopper incentives) and involuntarily (e.g. US Census or medical records).