15,477 research outputs found
An Approach to Model Checking of Multi-agent Data Analysis
The paper presents an approach to verification of a multi-agent data analysis
algorithm. We base correct simulation of the multi-agent system by a finite
integer model. For verification we use model checking tool SPIN. Protocols of
agents are written in Promela language and properties of the multi-agent data
analysis system are expressed in logic LTL. We run several experiments with
SPIN and the model.Comment: In Proceedings MOD* 2014, arXiv:1411.345
Secure Multiparty Computation with Partial Fairness
A protocol for computing a functionality is secure if an adversary in this
protocol cannot cause more harm than in an ideal computation where parties give
their inputs to a trusted party which returns the output of the functionality
to all parties. In particular, in the ideal model such computation is fair --
all parties get the output. Cleve (STOC 1986) proved that, in general, fairness
is not possible without an honest majority. To overcome this impossibility,
Gordon and Katz (Eurocrypt 2010) suggested a relaxed definition -- 1/p-secure
computation -- which guarantees partial fairness. For two parties, they
construct 1/p-secure protocols for functionalities for which the size of either
their domain or their range is polynomial (in the security parameter). Gordon
and Katz ask whether their results can be extended to multiparty protocols.
We study 1/p-secure protocols in the multiparty setting for general
functionalities. Our main result is constructions of 1/p-secure protocols when
the number of parties is constant provided that less than 2/3 of the parties
are corrupt. Our protocols require that either (1) the functionality is
deterministic and the size of the domain is polynomial (in the security
parameter), or (2) the functionality can be randomized and the size of the
range is polynomial. If the size of the domain is constant and the
functionality is deterministic, then our protocol is efficient even when the
number of parties is O(log log n) (where n is the security parameter). On the
negative side, we show that when the number of parties is super-constant,
1/p-secure protocols are not possible when the size of the domain is
polynomial
Verifying privacy by little interaction and no process equivalence
While machine-assisted verification of classical security goals such as confidentiality and authentication is
well-established, it is less mature for recent ones. Electronic voting protocols claim properties such as voter
privacy. The most common modelling involves indistinguishability, and is specified via trace equivalence in cryptographic extensions of process calculi. However, it has shown restrictions. We describe a novel model, based on unlinkability between two pieces of information. Specifying it as an extension to the Inductive Method allows us to establish voter privacy without the need for approximation or session bounding. The two
models and their latest specifications are contrasted
Formal verification of distributed deadlock detection algorithms
The problem of distributed deadlock detection has undergone extensive study. Formal verification of deadlock detection algorithms in distributed systems is an area of research that has largely been ignored. Instead, most proposed distributed deadlock detection algorithms have used informal or intuitive arguments, simulation or just neglect the entire aspect of verification of correctness; As a consequence, many of these algorithms have been shown incorrect. This research will abstract the notion of deadlock in terms of a temporal logic of actions and discuss the invariant and eventuality properties. The contributions of this research are the development of a distributed deadlock detection algorithm and the formal verification of this algorithm
Automated unique input output sequence generation for conformance testing of FSMs
This paper describes a method for automatically generating unique input output (UIO) sequences for FSM conformance testing. UIOs are used in conformance testing to verify the end state of a transition sequence. UIO sequence generation is represented as a search problem and genetic algorithms are used to search this space. Empirical evidence indicates that the proposed method yields considerably better (up to 62% better) results compared with random UIO sequence generation
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