My research interests lie at the intersection of Machine Learning, Distributed Systems, Security & Data privacy. My work has broadly combined principles of the design, analysis, and implementation of security and privacy for real-world systems.
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Over the past years, my interests have evolved to include topics in intrusion detection systems, secure e-payment systems, secure mobile agent systems, pervasive computing, peer to peer systems, applied cryptography and privacy enhancing technologies in a wide range of fields such as databases, healthcare, cloud computing, location based services, and social networks.
The goal of my research is to reconcile the need of data sharing and knowledge extraction with the increasing demand of privacy and security protection. To this end, I conducted research on a variety of topics, including privacy for recommendation services, private database outsourcing for data mash-up services, private pervasive computing monitoring applications, private community discovery, applied cryptography and distributed machine learning.
Building privacy-enhanced mechanisms based on differential privacy is a significant research challenge facing our information society. My research addresses this challenge with novel techniques that have broad impacts, my approach in conducting research in these areas considered using and advancing well established theories to fit to problems at hand, and applying them to system driven needs. Furthermore, I built primitives that consider application-specific requirements—as a main design principle, thus reducing constraints and complexities to achieve better functionality.
For an example of current research activities, please have a look at my Publications, and Presentations
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