Prof. Tamir Tassa

Discipline: Computer Science

Expert in: Privacy preserving computation

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Key words: Privacy-preserving data mining and publishing, multi-party computation, distributed constraint optimization problems, secure voting, collaborative filtering 

What are you currently researching?
The design of protocols, in diverse application domains, that enable several parties to perform computations over inputs that they hold while maintaining the privacy of those inputs.

How did you become involved in your research field?
I began my academic career as a mathematician who worked on partial differential equations. At some point I left the academic world and worked in hi-tech. When I returned to academia, I conducted research in cryptography (as that was a topic to which I was exposed during my hi-tech interim period). Initially I worked on security in broadcasting systems, but then I began studying privacy-preserving methods for data publishing, and later on, I began working on privacy-preserving computations, an area which I find fascinating.      

What inspired you to become a researcher?
The intellectual challenge that the problem at hand poses, the search for solutions, the interaction with my peers, who are also my friends, and the joy in achieving the goal and seeing our work acknowledged and accepted for publication.   

Which of your research findings would you like to highlight?
Recently my peers and I designed a secure voting system that can compute in a secure manner the outcomes of elections that are governed by score-based or order-based voting rules. Our system offers perfect privacy for the voters and it is very efficient. We have demonstrated the system in a premium conference on computer and communication security and we hope that we will be able to see our system implemented in real-life elections.
How does your research link to the challenges of today?
This is the era of big data, and one of the greatest challenges is to be able to extract valuable information from the vast volume of data. As such data is typically distributed among several parties, it is essential to devise techniques to mine valuable information, without breaching the privacy of the data owners or the data subjects. My research in recent years has focused on that challenge, and my peers and I design solutions for that challenge in various application domains.
What excites you about your research field?
It has theoretical aspects as well as practical challenges, and it is relevant to diverse application scenarios, such as recommendation systems, secure voting, optimization problems, and federated learning.