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       Moving object databases (MOD) have gained much interest in
       recent years due to the advances in mobile communications and
       positioning technologies. Study of MOD can reveal useful
       information (e.g., traffic patterns and congestion trends) that
       can be used in applications for the common benefit.  In order to
       mine and/or analyze the data, MOD must be published, which can
       pose a threat to the location privacy of a user. Indeed, based
       on prior knowledge of a user's location at several time
       points, an attacker can potentially associate that user to a
       specific moving object (MOB) in the published database and learn
       her position information at other time points.  In this paper,
       we study the problem of privacy-preserving publishing of moving
       object database. Unlike in microdata, we argue that in MOD,
       there does not exist a fixed set of quasi-identifier (QID)
       attributes for all the MOBs. Consequently the anonymization
       groups of MOBs (i.e., the sets of other MOBs within which to
       hide) may not be disjoint.  Thus, there may exist MOBs that can
       be identified explicitly by combining different anonymization
       groups. We illustrate the pitfalls of simple adaptations of
       classical k-anonymity and develop a notion which we prove is
       robust against privacy attacks. We propose two approaches,
       namely extreme- union and symmetric anonymization, to build
       anonymization groups that provably satisfy our proposed
       k-anonymity requirement, as well as yield low information loss.
       We ran an extensive set of experiments on large real-world and
       synthetic datasets of vehicular traffic. Our results demonstrate
       the effectiveness of our approach.