One of the most prominent techniques for indoor localization is called fingerprinting. However, the regular fingerprint localization schemes are not able to protect the privacy of the user and the service provider simultaneously. Several attempts have been proposed to overcome the issue, but many of them are insecure .
Provably secure privacy-preserving indoor localization can be achieved using secure two-party computation (STPC) techniques [2, 3]. However, these existing solutions still have severe disadvantages:  uses additively homomorphic encryption which results in substantial computational overhead in the online phase, and  relies on two non-colluding servers. A truly practical solution should work efficiently even in large-scale settings and without assuming non-colluding servers.
The goal of this thesis is to build highly efficient secure two-party computation protocols for privacy-preserving indoor localization in the client-server setting. This will be achieved by designing, analyzing, comparing, implementing, and benchmarking different schemes based on state-of-the-art STPC techniques that improve over [2, 3]. The aim is to compare and build different schemes that allow trade-offs between localization accuracy and efficiency, e.g., by altering the quantization of the signal strength .
- Good programming skills in C/C++
- Basic knowledge in Android development
- Basic knowledge of secure multi-party computation is beneficial
- High motivation + ability to work independently
- Knowledge of the English language, Git, LaTeX, etc. goes without saying
-  Z. Yang and K. Järvinen, (opens in new tab). In IEEE INFOCOM, 2018. The death and rebirth of privacy-preserving WiFi fingerprint localization with Paillier encryption
-  R. Nieminen and K. Järvinen. . In IEEE Transactions on Mobile Computing (TMC), 2020. Practical privacy-preserving indoor localization based on secure two-party computation
-  K. Järvinen, H. Leppäkoski, E. S. Lohan, P. Richter, T. Schneider, O. Tkachenko, and Z. Yang. (opens in new tab). In EuroS&P, 2019. PILOT: Practical privacy-preserving Indoor Localization using OuTsourcing
-  P. Richter, Z. Yang, O. Tkachenko, H. Leppäkoski, K. Järvinen, T. Schneider, and E. S. Lohan. (opens in new tab). In ICL-GNSS, 2018. Received signal strength quantization for secure indoor positioning via fingerprinting
- ( Raine Nieminen, M.Sc.email@example.com-…)
- ( Prof. Dr.-Ing. Thomas Schneiderschneider@encrypto.cs.tu-…)
Christopher van der Beets, Raine Nieminen and Thomas Schneider: (opens in new tab). In 19. International Conference on Security and Cryptography (SECRYPT'22), 2022. FAPRIL: Towards Faster Privacy-Preserving Fingerprint-Based Localization