Tracing Individual Public Transport Customers from an Anonymous Transaction Database

Data mining concepts are used frequently throughout the transportation research sector. This article examines the concept of the market basket technique as a means of gaining more insight into public transport users’ demands. The article proposes a method that uses various data attributes of passenger records to infer the same customer in a different week (i.e., attempts to track the same customer from week to week). The general idea behind the measure is that if two records are considered similar, ideally every trip in one customer record should have a close counterpart in the other record. The research develops a similarity function designed to maximize the percentage of positive ticket identification over a number of weeks. Once similarity has been established, customer travel patterns can be useful in helping the operator identify new routes, new timetables, and strategic decisions in relation to satisfying public transport customer demands

Applications of SAT solvers to cryptanalysis of hash functions

Several standard cryptographic hash functions were broken in 2005. Some essential building blocks of these attacks lend themselves well to automation by encoding them as CNF formulas, which are within reach of modern SAT solvers. In this paper we demonstrate effectiveness of this approach. In particular, we are able to generate full collisions for MD4 and MD5 given only the differential path and applying a (minimally modified) off-the-shelf SAT solver. To the best of our knowledge, this is the first example of a SAT-solver-aided cryptanalysis of a non-trivial cryptographic primitive. We expect SAT solvers to find new applications as a validation and testing tool of practicing cryptanalysts.