Insecurity of Transformation-Based Privacy-Preserving Linear Programming
Name
Alisa Pankova
Abstract
Applied mathematics is used in many real-world problems. Solving some of these problems may involve sensitive data. In this case, cryptographic techniques become necessary. Although it has been proven that any function can be computed securely, it is still a question how to do it efficiently. While it may be difficult to solve optimization tasks securely and efficiently in general, there may still be solutions for some particular classes of tasks, such as linear programming. This thesis gives an overview of the transformation-based privacy-preserving linear programming. The thesis introduces some problems of this approach that have been present in the previous works and demonstrates its insecurity. It presents concrete attacks against published methods following this approach. Possible methods of protection against these attacks are proposed. It has been proven that there are issues that cannot be resolved at all using the particular known class of efficient transformations that has been used before.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Peeter Laud and Margus Niitsoo
Defence year
2013