Seminar Topics

Smoothing is a technique that helps to study computational problems in cryptography. While it has been primarily applied to lattice-based problems, recent work has begun to explore its relevance in the context of coding problems.

In this seminar, you will learn about the different coding problems that appear in known reductions, as well as the techniques used to establish these reductions. Particular attention will be given to the challenges that arise when attempting to extend these techniques to more general classes of codes. These obstacles will be examined, discussed, and presented in detail.

Reference papers:

- Brakerski, Zvika, et al. "Worst-case hardness for LPN and cryptographic hashing via code smoothing." (https://eprint.iacr.org/2018/279)
- Yu, Yu, and Jiang Zhang. "Smoothing out binary linear codes and worst-case sub-exponential hardness for LPN." (https://eprint.iacr.org/2020/870)
- Debris–Alazard, Thomas, and Nicolas Resch. "Worst and average case hardness of decoding via smoothing bounds." (https://eprint.iacr.org/2022/1744)

The document exchange problem considers the scenario where two parties have slightly different versions of the same file, and one of them wants to update their version to match the other. Instead of sending the whole file, the idea is to share only a small description of the changes. Substring edits, where changes happen locally within the file, are a natural way to model these differences. This seminar will examine how coding theory can be applied to enhance the efficiency of document exchange under substring edits.

Post-quantum cryptography (PQC) has been an active area of research since the seminal work of Shor. 
Indeed, most public-key cryptography currently deployed (such as RSA and elliptic-curve-based schemes) is vulnerable to quantum adversaries. 
This applies in particular to public-key encryption (PKE) schemes.  
An attacker could record encrypted traffic and later decrypt it once a sufficiently capable quantum computer becomes available - a strategy known as "harvest now, decrypt later."

Post-quantum secure alternatives can be constructed from hard problems on codes and lattices. The recently standardized Kyber and HQC follow an encryption-based approach, while, e.g., NewHope is based on a key reconciliation mechanism:
Alice and Bob obtain noise variants of a common secret, and error correction removes this noise, allowing them to agree on the same shared key.

This project will survey constructions for exchanging a key in code-based and lattice-based cryptography. These constructions are to be categorized based on key properties, such as underlying metric, bandwidth requirements, and underlying assumptions.
An overview of the key techniques is to be developed, with particular focus on the question of whether they can be transferred from codes to lattices and vice versa. The project requires reading and understanding several references; a good starting point can be the following works:

Aguilar-Melchor, Carlos, et al. "Efficient encryption from random quasi-cyclic codes." IEEE Transactions on Information Theory 64.5 (2018): 3927-3943.

Bos, Joppe, et al. "CRYSTALS-Kyber: a CCA-secure module-lattice-based KEM." 2018 IEEE European symposium on security and privacy (EuroS&P). IEEE, 2018.

Alkim, Erdem, et al. "Post-quantum Key Exchange — A new hope." 25th USENIX security symposium (USENIX Security 16). 2016.

Fully Homomorphic Encryption (FHE) enables computations on encrypted data without decryption, providing a powerful tool for privacy-preserving applications.

In this seminar, the student will study the BFV scheme, a widely used homomorphic encryption construction. The main goal is to understand the underlying mathematics, the design rationale of BFV, and how it fits into the broader landscape of lattice-based cryptography.

The student is expected to:

• Review the foundational works by Brakerski and by Fan–Vercauteren.
• Summarize the scheme’s construction, security assumptions, and noise management.
• Present connections to applications and implementations.

References:

[1] Z. Brakerski, "Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP," Advances in Cryptology – CRYPTO 2012, Lecture Notes in Computer Science, vol. 7417, Springer, 2012, pp. 868–886. doi: 10.1007/978-3-642-32009-5_50

[2] J. Fan and F. Vercauteren, "Somewhat Practical Fully Homomorphic Encryption," IACR Cryptology ePrint Archive, vol. 2012/144, 2012. Available:

It has been widely acknowledged that machine learning models can leak a significant amount of (potentially private) information about their training data. Analyzing the amount of information leaked about the training data is important to judge the model's privacy. In practice, so-called membership inference attacks [1,2] are employed for such a privacy audit. A membership inference attack tries to predict whether a particular data sample was used in the training of a machine learning model. Besides empirical research, membership inference attacks have been put on a theoretical foundation through a Bayesian decision framework [3].

The goal of this seminar topic is to understand state-of-the-art membership inference attacks [1,2] and the Bayesian decision framework [3]. Students are encouraged to produce their own results using openly available implementations.

[1] N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis and F. Tramèr, "Membership Inference Attacks From First Principles," 2022 IEEE Symposium on Security and Privacy (SP), 2022.

[2] S. Zarifzadeh, P. Liu, R. Shokri, "Low-Cost High-Power Membership Inference Attacks," ICML 2024.

[3] A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, H. Jegou, "White-box vs Black-box: Bayes Optimal Strategies for Membership Inferenc," Proceedings of the 36th International Conference on Machine Learning, 2019.

Prerequisites

5G and beyond wireless communication systems gave/are expected to give rise to many new applications with stringent requirements that were not achievable with 4G or earlier systems. These requirements are: low latency, high reliability, battery life maximization among others.

To this extent, short blocklength coding has gained particular interest, and new research on efficient coding schemes is emerging. An idea previously discussed in the literature is the Ordered Statistics Decoder (OSD) [1]. Recent works in literature are addressing its strengths, its shortcomings and are suggesting ways to make this decoder more practical and efficient in the short blocklength regime.

The objective of the student is to study the seminal paper [1], that introduces the idea of ordered statistics decoding and be able to describe how the decoder works, what are its strengths, what are its limitations. Then, the student is expected to understand and explain the way of working of newer algorithms [2] [3] and explain if and how they improve performance.

[1] Soft-decision decoding of linear block codes based on ordered statistics | IEEE Journals & Magazine | IEEE Xplore

[2] Partial Ordered Statistics Decoding with Enhanced Error Patterns | IEEE Conference Publication | IEEE Xplore

[3] Box and match techniques applied to soft-decision decoding | IEEE Journals & Magazine | IEEE Xplore

Prerequisites

Field of Research & Contents of the Paper

Time-varying phase noise is of interest in many communication scenarios, including high-frequency wireless channels and coherent optical communications. 

In [1], the authors introduce an iterative receiver for channels distorted by phase noise. They use strong correlations in the phase noise process for mitigation. A downside is the requirement for pilot insertion, effectively decreasing spectral efficiency. 

Expectation propagation (EP) [2][3, Ch. 10, Sec. 10.7] has been recently used to enhance the receiver design, thereby mitigating the need for pilot insertion [4]. 

The task of this seminar paper is to

1. summarize the system model used in [1],[4],
2. analyze the approach, benefits, and drawbacks in [1],
3. explain the principles of EP, and
4. compare how EP modifies the receiver in [4] relative to [1]. 

A successful submission provides intuition for tasks 1-3 and a thorough analysis of the differences between [1] and [4] for task 4. 

[1] G. Colavolpe, A. Barbieri and G. Caire, "Algorithms for iterative decoding in the presence of strong phase noise," in IEEE Journal on Selected Areas in Communications, 2005.

[2] T. P. Minka, "Expectation Propagation for approximate Bayesian inference," in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, 2001. 

[3] C.M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006.

[4] E. Conti, A. Vannucci, A. Piemontese, G. Colavolpe, "Phase Noise Detection via Expectation Propagation and Related Algorithms", arXiv, 2024.

Organization of the Supervision

• After you have successfully applied for the topic, we will hold a kick-off meeting to discuss all necessary details for the upcoming work. After that, the responsibility for the seminar paper is all on you. You can always contact me, ask questions, and request meetings, and I am happy to assist you along the way. However, I need you to be proactive.
• I can provide comments on your work (e.g., the paper's outline and some paragraphs you have already written) at any time you need them and guide you as you prepare your paper and presentation. Regarding final drafts, I comment on one complete version of your paper. Ideally, this would mean that you do some work (literature research, the paper's outline, the body, the introduction, and the conclusion), and we discuss your results after every step. Ultimately, you provide me with one final draft, and I will comment on it. The draft must be handed in at least 10 days prior to the paper submission deadline. This method is more successful and instructive than writing a paper a few days before the deadline.
• Beware that when I comment on your work, this is not the same as a correction. My comments are suggestions for improving the quality of the work, but I cannot spend the time searching for and correcting every mistake that exists or could be made (this would also not align with the seminar's purpose).
• I expect solid research. There should be two significant references. Those are papers you know in detail and that majorly impact your work. Of course, they are always accompanied by supporting references.

Prerequisites

Which is the right equation to solve within the domain of fiber optics?

It all boils down to the application scenario, to the interplay between the duration of the transmission pulse, the channel dynamic, and the desired level of accuracy.

The most accurate and computationally demanding model is provided by Maxwell's equations, while other equations (like Manakov equations and Schrödinger equations) can be obtained within certain assumptions.

Tasks of the student is to review the subject, pointing out typical scenarios, their relevant equation, the assumptions within which it has been derived, and a numerical method to solve it.

The student can refer to [Menyuk99] and references therein.

REFERENCES: 

[Menyuk99] Menyuk, C. R. "Application of multiple-length-scale methods to the study of optical fiber transmission." Journal of Engineering Mathematics 36.1 (1999): 113-136.

Prerequisites

As there are continuously increasing throughput demands on fiber optical networks, many types of multiplexing systems have become a hot topic for research as there is a need to better utilize the bandwidth resources of the optical fiber and increase spectral efficiency, some of these allow of unique joint Digital Signal Processing. Carrier phase estimation is an important aspect of DSP-based receivers, through carrier phase estimation the effects of laser phase noise are mitigated, the effects of laser phase noise are in general more important as systems become more spectrally efficient. Traditionally carrier and phase recovery is done per channel basis using either blind or data aided methods, however some methods are able to exploit correlation between the channels and other such properties to perform joint carrier and phase recovery.

This topic focuses mainly on joint carrier and phase recovery algorithms and their performance. This topic looks at Frequency Comb-Based Transmission Systems and how in those systems the phase coherence between the lines can be exploited to simplify or increase the performance of digital carrier phase recovery algorithms and comparisons are made between different algorithms and different pilot distributions over the channels.

Papers:

1. Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing, Lars Lundberg et al

2. Joint Carrier Recovery for DSP Complexity Reduction in Frequency Comb-Based Superchannel Transceivers, Lars Lundberg et al

3. Pilot-Aided Joint-Channel Carrier-Phase Estimation in Space-Division Multiplexed Multicore Fiber Transmission, Arni F. Alfredsson et al                

4. Optimization of Pilot-Aided Joint Phase Recovery for Frequency Comb-Based Wideband Transmission, Gabriele Di Rosa et al

Interested student please contact Muhammad Ahmed Leghari for further discussion

Contact: ahmed.leghari@adtran.com

Optical communication systems are the backbone of current communication network as they allow fast and reliable information exchange. However, the advent of new generation applications and services require an ever increasing demand for higher capacity, lower cost, and lower energy consumption. According to discussions in [1], because of the difference in the growth rates between the fiber capacity and the traffic demand, there is expected to be a capacity shortage within the next decade. 

Among several options for increasing the capacity of installed optical fibers, exploiting new wavelength bands appears to be a promising and economical solution. Extending the transmission bandwidth from conventional band (C-band) to multi-band (MB), researchers are aiming to transmit information over the entire single mode fiber spectrum. This will increase the optical capacity and thus postpone the need to deploy new fibers. 

The challenges for the realization of such multiband networks lie both in the development of key components – as well as in the modelling of the physical and network layers. 

The modelling of UWB systems enables to optimize the design of a given optical communication link to achieve maximum throughput, including finding optimal signal launch power, data rates, modulation formats, number of channels, and, in the case of hybrid-amplified links, also the Raman amplifier’s pump powers and frequencies. The Gaussian noise closed-form model (GN-CFM) expressions [2] have increasingly been used for rapid evaluation of nonlinear interference (NLI) noise, which, together with amplified spontaneous emission (ASE) noise and transceiver noise, define the received SNR. GN-CFM, in turn, can be used to optimize optical link parameters.

The main paper of the seminar work [3] uses the GN model discussed in [4] [5] to calculate optimum launch power per channel with the presence of Raman amplification. The student’s task is to deliver the steps they used in paper [3] as well as to understand the mechanics of a hybrid amplification schemes and GN model defined in [4] [5].  

[1] Essiambre, René-Jean and Robert W. Tkach. “Capacity Trends and Limits of Optical Communication Networks.” Proceedings of the IEEE 100 (2012): 1035-1055.

[2] D. Semrau, R. I. Killey, and P. Bayvel, “A Closed-Form Approximation of the Gaussian Noise Model in the Presence of Inter-Channel Stimulated Raman Scattering,” Journal of Lightwave Technology, vol. 37, pp. 1924–1936, May 2019.

[3] H. Buglia, E. Sillekens, L. Galdino, R. I. Killey, and P. Bayvel, "Impact of launch power optimisation in hybrid-amplified links," 2024. 

[4]H. Buglia, E. Sillekens, L. Galdino, R. I. Killey, and P. Bayvel, “Throughput maximisation in ultra-wideband hybrid-amplified links”, in Optical Fiber Communications Conference, 2024, Tu3H.5.

[5]H. Buglia, M. Jarmolovicius, L. Galdino, R. I. Killey, ? and P. Bayvel, “A closed-form expression for the Gaussian noise model in the presence of Raman amplification”, Journal of Lightwave Technology, vol. 42, no. 2, pp. 636–648, 2024. DOI: 10.1109/JLT.2023.3315127.