Radboud Digital Security group Lunch Talk homepage

Welcome to the site of the talks organised by Radboud Digital Security group. We organize a talk every Wednesday at 12:30.

Objectives:

Policy:

Resources:

Upcoming talks

  • Monday, 28th of April 2025 at 12:30 in lecture room in Huygensbuilding (HG00.071, ground floor)
    DiS Lunch by Guang Gong, University of Waterloo, Canada

    Efficient Implementations of zkSNARK-Based Post-Quantum Digital Signatures

    A zero-knowledge proof is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a mathematical statement (typically in NP) without revealing any secret inputs.
    A particularly important subclass is the zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK), which is tailored for proving statements about arithmetic circuits.
    zkSNARKs have gained prominence in blockchain privacy applications and are increasingly being explored for post-quantum digital signature algorithms (DSAs).
    The security of post-quantum zkSNARK-based DSAs relies primarily on the sum-check protocol, which is reduced to the Fast Reed-Solomon Interactive Oracle Proof of Proximity (FRI) for checking proximity to RS codewords, along with cryptographic hash functions for authenticating these codewords.
    The construction of such schemes typically involves three main steps: 1) Encoding a one-way function as a Rank-1 Constraint System (R1CS);
    2) Interpolating a polynomial via the inverse Fast Fourier Transform (IFFT) to represent the R1CS relation; 3) Running FRI to check the polynomial's validity at a random evaluation point.
    In this talk, I will begin by walking through the standard zkSNARK construction process and then present our recent results on improving the efficiency of the FRI protocol.
    In particular, I will introduce a new technique that integrates multiple Merkle tree commitments into a single unified Merkle tree, significantly reducing the computational cost for both the prover and verifier, as well as lowering the overall communication overhead (e.g., signature size).
    I will illustrate the effectiveness of this approach through concrete examples such as Preon—a Round 1 candidate in NIST’s Additional Post-Quantum Cryptography Digital Signature (PQC DSA) project—which uses AES circuits and the Aurora zkSNARK (or alternatively Polaris) for its proof system.
    I will also discuss GPU-based performance evaluations for these schemes, along with comparisons to FAEST, a Round 2 DSA candidate.

  • Wednesday, 30th of April 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by Luqman Zagi

    Referrer Policy: Implementation and Circumvention

    The Referrer Policy (RP) standard makes it possible for websites to control how much information will be shared in the Referer [sic] header.
    In this study, we investigate the implementation and circumvention of the Referrer Policy standard across 27,750 distinct websites and over 100K pages from three vantage points: the United States, Singapore, and the Netherlands.
    Our findings reveal that 48.38% of websites implement document-wide referrer policies, and 13.39% apply element-specific referrer policies.
    The majority of the sites (43.81%) use the Referrer-Policy HTTP response header to set a document-wide policy, while 11.09% use HTML meta tags.
    Even on websites with restrictive referrer policies, scripts can access the full page URL and exfiltrate it --- which we label as a referrer policy circumvention.
    We identified RP circumventions on 77.20% of websites often carried out by third-party advertising and analytics scripts, including Google Analytics, Facebook, and TikTok Pixel.
    While the ability to manage referrer information and the adoption of more privacy-focused default policies represent positive gains for user privacy, the widespread circumvention of these measures by third-party scripts remains to be a problem.
    We recommend implementing technical measures to restrict script access in order to address this privacy and security issue.

  • Wednesday, 7th of May 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by Solane El Hirch

    TBA

  • Wednesday, 14th of May 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by Joan Daemen

    TBA

  • Wednesday, 21st of May 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by Vianney Lapotre

    TBA

  • Wednesday, 28th of May 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by TBA

    TBA

  • Wednesday, 4th of June 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by Jan Schoone

    TBA

  • Wednesday, 11th of June 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by TBA

    TBA

  • Friday, 20th of June 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by Sengim Karayalcin

    It's Not Just a Phase: On Investigating Phase Transitions in Deep Learning-based Side-channel Analysis

    Side-channel analysis (SCA) represents a realistic threat where the attacker can observe unintentional information to obtain secret data.
    Evaluation labs also use the same SCA techniques in the security certification process.
    The results in the last decade have shown that machine learning, especially deep learning, is an extremely powerful SCA approach, allowing the breaking of protected devices while achieving optimal attack performance.
    Unfortunately, deep learning operates as a black-box, making it less useful for security evaluators who must understand how attacks work to prevent them in the future.
    This work demonstrates that mechanistic interpretability can effectively scale to realistic scenarios where relevant information is sparse and well-defined interchange interventions to the input are impossible due to side-channel protections.
    Concretely, we reverse engineer the features the network learns during phase transitions, eventually retrieving secret masks, allowing us to move from black-box to white-box evaluation.

  • Wednesday, 25th of June 2025 at 12:30 in the big lecture room in Mercator 1 (MERC1_00.28, ground floor)
    DiS Lunch by TBA

    TBA

  • Past talks