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be published, and agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received

the signals al-though they are encrypted. This paper focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the client without getting any information about the signal itself and the final result of the classification

In the last years, DNA sequencing techniques have advanced to the point that DNA identification and paternity testing has become almost a commodity. Due to the critical nature of DNA related data, this causes substantial privacy issues. In this paper, we introduce cryptographic privacy enhancing

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Unwanted traffic is a major concern in the Internet today. Unwanted traffic includes Denial of Service attacks, worms, and spam. Identifying and mitigating unwanted traffic costs businesses many billions of USD every year. The process of identifying this traffic is called anomaly detection, and Intrusion Detection Systems (IDS’es) are among the most prevalent techniques. IDS’es, such as Snort, allow users to write “rules ” that specify the properties of traffic that should be detected and the corrective action to be taken in response. Unfortunately, applying these rules in an online setting can be prohibitively expensive for large networks, such as Tier-1 ISPs, which may have tens of thousands of links and many Gbps of traffic. In the first chapter of this thesis we present a system that leverages machine learning algorithms to detect the same type of unwanted traffic as Snort, but on summarized data for faster processing. Our results demonstrate that this system can effectively learn to classify many Snort rules with a high degree of accuracy. Unfortunately, distinguishing good traffic from unwanted traffic is challenging even in an offline setting because many types of unwanted traffic traffic, such as

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privacy-preserving filtering based on encrypted matching operators. It is based on a pre-filtering stage that can be combined with containment graphs, if available. Our experiments indicate that pre-filtering is able to significantly reduce the num-ber of encrypted matching for a variety of workloads

of data is implemented by specializing or detailing the level of information in a top-down manner until a minimum privacy requirement is violated. This top-down specialization is natural and efficient for handling both categorical and continuous attributes. Our approach exploits the fact that data usually