Motivation

Private Information Retrieval (PIR) allows a client to privately access an entry in a public database held by a server without leaking information about the client’s query. Example applications for PIR are private messaging apps [2] and privacy-friendly lookups in medical or patent databases. Modern multi-server PIR schemes outperform single server PIR in online runtime by reducing the amount of data each server has to touch during a request, but require that a subset of the servers are non-colluding. While great effort has been put into optimizing the online runtime, the storage requirements are expensive. A promising approach to reduce the storage for a single server is a centralized, powerful computer cluster that is optimized for offline runtime and distributes the necessary data among the servers.

Goal

The goal of this thesis is to integrate a centralized server in a PIR system based on RAID-PIR [1, 2]. This server shall provide the offline computation for the much less powerful PIR servers. At the end, the effectiveness of the centralized solution shall be benchmarked.

Tasks

Firstly, a detailed literature study shall be conducted to gain a detailed knowledge of state of the art PIR schemes. The second step is a highly parallelized and optimized CUDA implementation to minimize the offline computation. Afterwards, the implementation shall be deployed and measured on a GPU cluster such as the Nvidia DGX-2 and/or a GPU node of the Lichtenberg High Performance Computer at TU Darmstadt. Finally, a comparison of the efficiency of the implemented system compared to previous work should be conducted by benchmarking runtimes and communication.

Requirements

• Good programming skills in C/C++
• Basic knowledge of parallelization and CUDA are desirable
• High motivation + ability to work independently
• Knowledge of the English language, Git, LaTeX, etc. goes without saying

References

[1] Daniel Demmler, Amir Herzberg, and Thomas Schneider. RAID-PIR: Practical multi-server PIR. In 6. ACM Cloud Computing Security Workshop (CCSW’14), pages 45–56. ACM, 2014. PDF. (PDF file) (opens in new tab)

[2] Daniel Demmler, Marco Holz, and Thomas Schneider. OnionPIR: Effective protection of sensitive metadata in online communication networks. In 15. International Conference on Applied Cryptography and Network Security (ACNS’17), pages 599–619. Springer, 2017. PDF. (PDF file) (opens in new tab)

Core data