The table below lists the public set of my currently available research and working opportunities for you. Please do not hesitate to contact me for potential Bachelor or Master Theses, as well as Research Internships, if you are interested in working in one of my research domains. Should your research interest not be listed right now, let us have a personal conversation in which we may identify and discuss suitable topics.
If you have not found a suitable topic but would still like to write your thesis with us, please contact student-thesis.eisec(at)lists.lrz.de and include a short description of the research area you are interested in. Please always attach your current transcript of records and a brief CV so that we can assess your suitability for potential topics.
Analyzing Weight Distributions (in BIKE) via Syndrome Information
Beschreibung
BIKE (Bit Flipping Key Encapsulation) is a post-quantum key exchange scheme based on quasi-cyclic moderate-density parity-check (QC-MDPC) codes. Security relies on the hardness of decoding random linear codes, where an attacker only knows the public matrix H, the syndrome s, and the exact weight of the error vector.
In this project the student will generate large datasets of BIKE ciphertexts and corresponding error vectors, and design experiments to analyze whether the weight (or distribution) of the error vector can be predicted directly from the syndrome and the parity-check matrix.
This includes:
Implementing dataset generation with fixed public keys and varying error vectors
Designing statistical or machine-learning based approaches to estimate error weights
Evaluating how predictable the error structure is and whether such predictability could weaken BIKE’s assumed hardness
Voraussetzungen
Good understanding of (code-based) cryptography basics
Programming skills in Python or C.
Interest in post-quantum cryptography and side-channel/security analysis.
