Current Lectures, Labs, and Seminars

PrivTech WS16/17

Past Lectures, Labs, and Seminars

PrivTech WS15/16

Open Thesis Topics

3 Entries found


13.03.2017

Web application for privacy-preserving scheduling

Bachelor Thesis, Master Thesis


Web applications currently used for scheduling meetings - such as Doodle - do not protect the privacy of their users, i.e., compute the results based on the clear inputs of the participants. Though privacy-friendly variants of Doodle already exist based on public-key encryption [TUD], these solutions assume a trusted party whose private key can be used to decrypt all inputs. Secure computation, which allows two parties to jointly compute a function while both parties keep their inputs private, is a promising approach to provide privacy for the users' inputs without the use of a trusted poll initiator.

13.03.2017

Web application for privacy-preserving assignments

Bachelor Thesis, Master Thesis


The assignment problem is a combinatorial optimization problem which can be solved in polynomial time using the Hungarian algorithm. An example for this problem is as follows: n students register for a seminar where n topics are available. The students provide their preferences to all available topics, which are associated with some costs (the happiness of the student when assigned to that topic): e.g., a student's first preference will cost n, while his/her least preference will cost 1. Any student can be assigned to any topic, all topics have to be assigned exactly to one student and each student needs to be assigned to exactly one topic in such a way that the total cost (happiness) is maximized. Such assignment problems can be solved without using a trusted third party in a privacy-preserving manner. In order to achieve this, secure computation techniques can be utilized. The main goal is to enable students to bid for their preferred topics and get the result without anyone else learning their preferences.

Secure Multi-Party Computation (SMPC) allows multiple parties to compute a publicly known function while keeping their inputs private. This privacy property is desirable, e.g., in the context of distributed Genome-Wide Association Studies (GWAS). In GWAS, researchers try to find associations between variations in genomes and traits (e.g. diseases) by computing certain statistics on sequenced genome data of case and control groups. Since genome sequencing is still comparatively expensive and meaningful GWAS results require large participant groups, it is reasonable for research institutes to collaborate and share their data sets for this purpose. However, participants do not want their genome data to be given out of hand, since data leakage could lead, for example, to discrimination by health insurance companies.

ABY is a widely used framework for compiling function descriptions into highly efficient privacy-preserving protocols in the SMPC special case of only two parties. Therefore, it could be employed to create protocols for securely computing the statistics needed for GWAS conducted by two collaborating research institutes. Using the framework, functions can be described as circuits by plugging together different kind of high-level gates (e.g. ADD and MUL) that operate on encrypted or secret shared input data. There exist arithmetic gates for integer and IEEE 754 floating point input values. However, currently only one type of gate can be used throughout a computation, i.e. for integer inputs only integer operations and likewise for floating point inputs only floating point operations can be performed. This is not desirable in cases where floating point operations on integer inputs are only necessary in the final parts of a computation since the complexity of floating point gates leads to much higher run-times.


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