Automating quantitative information flow

PhDUnprecedented quantities of personal and business data are collected, stored, shared, and processed by countless institutions all over the world. Prominent examples include sharing personal data on social networking sites, storing credit card details in every store, tracking customer preferences of supermarket chains, and storing key personal data on biometric passports. Confidentiality issues naturally arise from this global data growth. There are continously reports about how private data is leaked from confidential sources where the implications of the leaks range from embarrassment to serious personal privacy and business damages. This dissertation addresses the problem of automatically quantifying the amount of leaked information in programs. It presents multiple program analysis techniques of different degrees of automation and scalability. The contributions of this thesis are two fold: a theoretical result and two different methods for inferring and checking quantitative information flows are presented. The theoretical result relates the amount of possible leakage under any probability distribution back to the order relation in Landauer and Redmond’s lattice of partitions [35]. The practical results are split in two analyses: a first analysis precisely infers the information leakage using SAT solving and model counting; a second analysis defines quantitative policies which are reduced to checking a k-safety problem. A novel feature allows reasoning independent of the secret space. The presented tools are applied to real, existing leakage vulnerabilities in operating system code. This has to be understood and weighted within the context of the information flow literature which suffers under an apparent lack of practical examples and applications. This thesis studies such “real leaks” which could influence future strategies for finding information leaks