A framework for proving the correctness of cryptographic protocol properties by linear temporal logic

In this paper, a framework for cryptographic protocol analysis using linear temporal logic is proposed. The framework can be used to specify and analyse security protocols. It aims to investigate and analyse the security protocols properties that are secure or have any flaws. The framework extends the linear temporal logic by including the knowledge of participants in each status that may change over the time. It includes two main parts, the Language of Temporal Logic (LTL) and the domain knowledge. The ability of the framework is demonstrated by analysing the Needham-Schroeder public key protocol and the Andrew Secure RPC protocol as examples

Knowledge Flow Analysis for Security Protocols

Knowledge flow analysis offers a simple and flexible way to find flaws in security protocols. A protocol is described by a collection of rules constraining the propagation of knowledge amongst principals. Because this characterization corresponds closely to informal descriptions of protocols, it allows a succinct and natural formalization; because it abstracts away message ordering, and handles communications between principals and applications of cryptographic primitives uniformly, it is readily represented in a standard logic. A generic framework in the Alloy modelling language is presented, and instantiated for two standard protocols, and a new key management scheme.Comment: 20 page

Privacy Architectures: Reasoning About Data Minimisation and Integrity

Privacy by design will become a legal obligation in the European Community if the Data Protection Regulation eventually gets adopted. However, taking into account privacy requirements in the design of a system is a challenging task. We propose an approach based on the specification of privacy architectures and focus on a key aspect of privacy, data minimisation, and its tension with integrity requirements. We illustrate our formal framework through a smart metering case study.Comment: appears in STM - 10th International Workshop on Security and Trust Management 8743 (2014

Probabilistic Algorithmic Knowledge

The framework of algorithmic knowledge assumes that agents use deterministic knowledge algorithms to compute the facts they explicitly know. We extend the framework to allow for randomized knowledge algorithms. We then characterize the information provided by a randomized knowledge algorithm when its answers have some probability of being incorrect. We formalize this information in terms of evidence; a randomized knowledge algorithm returning ``Yes'' to a query about a fact \phi provides evidence for \phi being true. Finally, we discuss the extent to which this evidence can be used as a basis for decisions.Comment: 26 pages. A preliminary version appeared in Proc. 9th Conference on Theoretical Aspects of Rationality and Knowledge (TARK'03