Efficiently deciding equivalence for standard primitives and phases

International audiencePrivacy properties like anonymity or untraceability are now well identified, desirable goals of many security protocols. Such properties are typically stated as equivalence properties. However, automatically checking equivalence of protocols often yields efficiency issues. We propose an efficient algorithm, based on graph planning and SAT-solving. It can decide equivalence for a bounded number of sessions, for protocols with standard cryptographic primitives and phases (often necessary to specify privacy properties), provided protocols are well-typed, that is encrypted messages cannot be confused. The resulting implementation , SAT-Equiv, demonstrates a significant speed-up w.r.t. other existing tools that decide equivalence, covering typically more than 100 sessions. Combined with a previous result, SAT-Equiv can now be used to prove security, for some protocols, for an unbounded number of sessions

Exploiting replication in distributed systems

Techniques are examined for replicating data and execution in directly distributed systems: systems in which multiple processes interact directly with one another while continuously respecting constraints on their joint behavior. Directly distributed systems are often required to solve difficult problems, ranging from management of replicated data to dynamic reconfiguration in response to failures. It is shown that these problems reduce to more primitive, order-based consistency problems, which can be solved using primitives such as the reliable broadcast protocols. Moreover, given a system that implements reliable broadcast primitives, a flexible set of high-level tools can be provided for building a wide variety of directly distributed application programs

Partial Order Reduction for Security Protocols

Security protocols are concurrent processes that communicate using cryptography with the aim of achieving various security properties. Recent work on their formal verification has brought procedures and tools for deciding trace equivalence properties (e.g., anonymity, unlinkability, vote secrecy) for a bounded number of sessions. However, these procedures are based on a naive symbolic exploration of all traces of the considered processes which, unsurprisingly, greatly limits the scalability and practical impact of the verification tools. In this paper, we overcome this difficulty by developing partial order reduction techniques for the verification of security protocols. We provide reduced transition systems that optimally eliminate redundant traces, and which are adequate for model-checking trace equivalence properties of protocols by means of symbolic execution. We have implemented our reductions in the tool Apte, and demonstrated that it achieves the expected speedup on various protocols

Data Minimisation in Communication Protocols: A Formal Analysis Framework and Application to Identity Management

With the growing amount of personal information exchanged over the Internet, privacy is becoming more and more a concern for users. One of the key principles in protecting privacy is data minimisation. This principle requires that only the minimum amount of information necessary to accomplish a certain goal is collected and processed. "Privacy-enhancing" communication protocols have been proposed to guarantee data minimisation in a wide range of applications. However, currently there is no satisfactory way to assess and compare the privacy they offer in a precise way: existing analyses are either too informal and high-level, or specific for one particular system. In this work, we propose a general formal framework to analyse and compare communication protocols with respect to privacy by data minimisation. Privacy requirements are formalised independent of a particular protocol in terms of the knowledge of (coalitions of) actors in a three-layer model of personal information. These requirements are then verified automatically for particular protocols by computing this knowledge from a description of their communication. We validate our framework in an identity management (IdM) case study. As IdM systems are used more and more to satisfy the increasing need for reliable on-line identification and authentication, privacy is becoming an increasingly critical issue. We use our framework to analyse and compare four identity management systems. Finally, we discuss the completeness and (re)usability of the proposed framework

Statistical Zero Knowledge and quantum one-way functions

One-way functions are a very important notion in the field of classical cryptography. Most examples of such functions, including factoring, discrete log or the RSA function, can be, however, inverted with the help of a quantum computer. In this paper, we study one-way functions that are hard to invert even by a quantum adversary and describe a set of problems which are good such candidates. These problems include Graph Non-Isomorphism, approximate Closest Lattice Vector and Group Non-Membership. More generally, we show that any hard instance of Circuit Quantum Sampling gives rise to a quantum one-way function. By the work of Aharonov and Ta-Shma, this implies that any language in Statistical Zero Knowledge which is hard-on-average for quantum computers, leads to a quantum one-way function. Moreover, extending the result of Impagliazzo and Luby to the quantum setting, we prove that quantum distributionally one-way functions are equivalent to quantum one-way functions. Last, we explore the connections between quantum one-way functions and the complexity class QMA and show that, similarly to the classical case, if any of the above candidate problems is QMA-complete then the existence of quantum one-way functions leads to the separation of QMA and AvgBQP.Comment: 20 pages; Computational Complexity, Cryptography and Quantum Physics; Published version, main results unchanged, presentation improve

ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives

We introduce ShapeCoder, the first system capable of taking a dataset of shapes, represented with unstructured primitives, and jointly discovering (i) useful abstraction functions and (ii) programs that use these abstractions to explain the input shapes. The discovered abstractions capture common patterns (both structural and parametric) across a dataset, so that programs rewritten with these abstractions are more compact, and suppress spurious degrees of freedom. ShapeCoder improves upon previous abstraction discovery methods, finding better abstractions, for more complex inputs, under less stringent input assumptions. This is principally made possible by two methodological advancements: (a) a shape-to-program recognition network that learns to solve sub-problems and (b) the use of e-graphs, augmented with a conditional rewrite scheme, to determine when abstractions with complex parametric expressions can be applied, in a tractable manner. We evaluate ShapeCoder on multiple datasets of 3D shapes, where primitive decompositions are either parsed from manual annotations or produced by an unsupervised cuboid abstraction method. In all domains, ShapeCoder discovers a library of abstractions that captures high-level relationships, removes extraneous degrees of freedom, and achieves better dataset compression compared with alternative approaches. Finally, we investigate how programs rewritten to use discovered abstractions prove useful for downstream tasks