Browsing by Author "CAO, Jianneng"

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    ASSIST: Access Controlled Ship Identification Streams
    (2013-02-08T04:32:24Z) CAO, Jianneng; KISTER, Thomas; XIANG, Shili; MALHOTRA, Baljeet; TAN, Wee-Juan; TAN, Kian-Lee; BRESSAN, Stéphane
    The International Maritime Organization (IMO) requires a majority of cargo and passenger ships to use the Automatic Identification System (AIS) for navigation safety and traffic control. Distributing live AIS data on the Internet can offer a global view for both operational and analytical purposes to port authorities, shipping and insurance companies, cargo owners and ship captains and other stakeholders. Yet, uncontrolled, this distribution can seriously undermine navigation safety and security and the privacy of the various stakeholders. In this paper we present ASSIST, an application system based on our recently proposed access control framework, to protect streaming data from unauthorized access. Furthermore, we have implemented ASSIST on top of StreamInsight, a commercial stream engine. The extensive experimental results show that our solution is more effective and efficient than existing approaches.
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    CASTLE: Continuously Anonymizing Data Streams
    (2008-02-26) CAO, Jianneng; CARMINATI, Barbara; FERRARI, Elena; TAN, Kian Lee
    Most of existing privacy preserving techniques such as k-anonymity methods, are designed for static data sets. As such, they cannot be applied to streaming data which are continuous, transient and usually unbounded. Moreover, in streaming applications, there is a need to offer strong guarantees on the maximum allowed delay between incoming data and the corresponding anonymized output. To cope with these requirements, in this paper, we present CASTLE (Continuously Anonymizing STreaming data via adaptive cLustEring), a cluster-based scheme that anonymizes data streams on-the-fly and, at the same time, ensures the freshness of the anonymized data by satisfying specified delay constraints. We further show how CASTLE can be easily extended to handle l-diversity. Our extensive performance study shows that CASTLE is efficient and effective wrt the quality of the output data.