arXiv subjects

Attila A Yavuz

Publications and source records attributed to Attila A Yavuz.

5 recordsLinked to original sources

arXiv2026

A Lightweight Post-Quantum Authentication Framework for 5G Base Station Bootstrapping

The absence of authenticated bootstrapping between User Equipments (UEs) and Base Stations (BSs) in 5G leaves System Information Block (SIB) broadcasts unprotected, enabling fake BS attacks, man-in-the-middle interception, and spoofed emergency alerts. Prior efforts such as Public Key Infrastructure (PKI)-based certificate chains, token-based schemes, and identity-based signatures either impose overhead exceeding 5G's strict packet-size constraints or lack post-quantum (PQ) security. Direct NIST-PQC integration is also infeasible; current standards, such as ML-DSA requires 34 fragmented SIB1 packets and up to 5,282\,ms end-to-end delay, and FN-DSA still requires 13 fragments and up to 1,920\,ms. We propose EMULSION, a symmetric chained publicly verifiable authentication framework for 5G/6G BS broadcast authentication. EMULSION is the first framework to exploit native 5G architectural features: fixed SIB transmission windows, millisecond-level time synchronization, and eSIM/USIM credential management to achieve genuine PQ security at symmetric-key efficiency. It uses a timed stream loss-tolerant authentication with HMAC chain anchored by a compact PQ signature~(MAYO) applied once per epoch, fitting authentication within a single packet with no fragmentation and eliminating certificate transmission entirely. Unlike prior schemes, EMULSION extends to the full SIB family (SIB1-SIB21) at no additional per-broadcast cost, demonstrated over the air on SIB1. Evaluated on a real over-the-air 5G testbed, EMULSION achieves 33x lower end-to-end delay and 31x less communication overhead than ML-DSA, and 12x lower delay and 5.4x less overhead than FN-DSA. We formally prove the security of EMULSION and open-source its implementation for public testing and adaptation.

cs.CR↗

arXiv2026

Future-Proofing Authentication Against Insecure Bootstrapping for 5G Networks: Feasibility, Resiliency, and Accountability

The 5G protocol lacks a robust base station (BS) authentication mechanism during the initial bootstrapping phase, leaving it susceptible to fake BSs, spoofed broadcasts, and large-scale manipulation of System Information Blocks (SIBs). Existing solutions incur high communication overhead, rely on centralized trust, and lack accountability and long-term breach resiliency. Given the inevitability of BS compromise and the severe impact of forged SIBs as the root of trust (e.g., fake alerts, tracking, false roaming), distributed trust, verifiable forgery detection, and audit logging are essential yet remain largely unexplored. These challenges are further amplified by the emergence of quantum-capable adversaries. While NIST Post-Quantum Cryptography (PQC) standards are widely viewed as a path toward long-term security, their feasibility under 5G's strict packet-size, latency, and broadcast constraints has not been systematically studied. This work presents, to our knowledge, the first comprehensive network-level performance characterization of integrating NIST-PQC standards and conventional digital signatures into 5G BS authentication, showing that direct PQC adoption is impractical due to excessive signature sizes, fragmentation, and protocol-level delays. To address these challenges, we propose BORG, a future-proof authentication framework based on a Hierarchical Identity-Based Threshold Signature with Fail-Stop (HITFS) properties. BORG distributes trust across multiple BSs via threshold signing, enables post-mortem verifiable forgery detection, and provides tamper-evident, PQ-secure audit logging, while maintaining compact signatures that fit within a single SIB1 packet without fragmentation and incurring minimal UE overhead, as validated through our real over-the-air 5G testbed implementation.

cs.CR↗

arXiv2025

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models

Federated learning (FL) enhances privacy by keeping user data on local devices. However, emerging attacks have demonstrated that the updates shared by users during training can reveal significant information about their data. This has greatly thwart the adoption of FL methods for training robust AI models in sensitive applications. Differential Privacy (DP) is considered the gold standard for safeguarding user data. However, DP guarantees are highly conservative, providing worst-case privacy guarantees. This can result in overestimating privacy needs, which may compromise the model's accuracy. Additionally, interpretations of these privacy guarantees have proven to be challenging in different contexts. This is further exacerbated when other factors, such as the number of training iterations, data distribution, and specific application requirements, can add further complexity to this problem. In this work, we proposed a framework that integrates a human entity as a privacy practitioner to determine an optimal trade-off between the model's privacy and utility. Our framework is the first to address the variable memory requirement of existing DP methods in FL settings, where resource-limited devices (e.g., cell phones) can participate. To support such settings, we adopt a recent DP method with fixed memory usage to ensure scalable private FL. We evaluated our proposed framework by fine-tuning a BERT-based LLM model using the GLUE dataset (a common approach in literature), leveraging the new accountant, and employing diverse data partitioning strategies to mimic real-world conditions. As a result, we achieved stable memory usage, with an average accuracy reduction of 1.33% for $ε= 10$ and 1.9% for $ε= 6$, when compared to the state-of-the-art DP accountant which does not support fixed memory usage.

cs.LG↗

arXiv2024

Fast and Post-Quantum Authentication for Real-time Next Generation Networks with Bloom Filter

Large-scale next-generation networked systems like smart grids and vehicular networks facilitate extensive automation and autonomy through real-time communication of sensitive messages. Digital signatures are vital for such applications since they offer scalable broadcast authentication with non-repudiation. Yet, even conventional secure signatures (e.g., ECDSA, RSA) introduce significant cryptographic delays that can disrupt the safety of such delay-aware systems. With the rise of quantum computers breaking conventional intractability problems, these traditional cryptosystems must be replaced with post-quantum (PQ) secure ones. However, PQ-secure signatures are significantly costlier than their conventional counterparts, vastly exacerbating delay hurdles for real-time applications. We propose a new signature called Time Valid Probabilistic Data Structure HORS (TVPD-HORS) that achieves significantly lower end-to-end delay with a tunable PQ-security for real-time applications. We harness special probabilistic data structures as an efficient one-way function at the heart of our novelty, thereby vastly fastening HORS as a primitive for NIST PQ cryptography standards. TVPD-HORS permits tunable and fast processing for varying input sizes via One-hash Bloom Filter, excelling in time valid cases, wherein authentication with shorter security parameters is used for short-lived yet safety-critical messages. We show that TVPD-HORS verification is 2.7x and 5x faster than HORS in high-security and time valid settings, respectively. TVPD-HORS key generation is also faster, with a similar signing speed to HORS. Moreover, TVPD-HORS can increase the speed of HORS variants over a magnitude of time. These features make TVPD-HORS an ideal primitive to raise high-speed time valid versions of PQ-safe standards like XMSS and SPHINCS+, paving the way for real-time authentication of next-generation networks.

cs.CR↗

arXiv2024

Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging

The shift towards efficient and automated data analysis through Machine Learning (ML) has notably impacted healthcare systems, particularly Radiomics. Radiomics leverages ML to analyze medical images accurately and efficiently for precision medicine. Current methods rely on Deep Learning (DL) to improve performance and accuracy (Deep Radiomics). Given the sensitivity of medical images, ensuring privacy throughout the Deep Radiomics pipeline-from data generation and collection to model training and inference-is essential, especially when outsourced. Thus, Privacy-Enhancing Technologies (PETs) are crucial tools for Deep Radiomics. Previous studies and systematization efforts have either broadly overviewed PETs and their applications or mainly focused on subsets of PETs for ML algorithms. In Deep Radiomics, where efficiency, accuracy, and privacy are crucial, many PETs, while theoretically applicable, may not be practical without specialized optimizations or hybrid designs. Additionally, not all DL models are suitable for Radiomics. Consequently, there is a need for specialized studies that investigate and systematize the effective and practical integration of PETs into the Deep Radiomics pipeline. This work addresses this research gap by (1) classifying existing PETs, presenting practical hybrid PETS constructions, and a taxonomy illustrating their potential integration with the Deep Radiomics pipeline, with comparative analyses detailing assumptions, architectural suitability, and security, (2) Offering technical insights, describing potential challenges and means of combining PETs into the Deep Radiomics pipeline, including integration strategies, subtilities, and potential challenges, (3) Proposing potential research directions, identifying challenges, and suggesting solutions to enhance the PETs in Deep Radiomics.

cs.CR↗