arXiv subjects

Sayandeep Saha

Publications and source records attributed to Sayandeep Saha.

7 recordsLinked to original sources

arXiv2025

Addendum: Systematic Evaluation of Randomized Cache Designs against Cache Occupancy

In the main text published at USENIX Security 2025, we presented a systematic analysis of the role of cache occupancy in the design considerations for randomized caches (from the perspectives of performance and security). On the performance front, we presented a uniform benchmarking strategy that allows for a fair comparison among different randomized cache designs. Likewise, from the security perspective, we presented three threat assumptions: (1) covert channels; (2) process fingerprinting side-channel; and (3) AES key recovery. The main takeaway of our work is an open problem of designing a randomized cache of comparable efficiency with modern set-associative LLCs, while still resisting both contention-based and occupancy-based attacks. This note is meant as an addendum to the main text in light of the observations made in [2]. To summarize, the authors in [2] argue that (1) L1d cache size plays a role in adversarial success, and that (2) a patched version of MIRAGE with randomized initial seeding of global eviction map prevents leakage of AES key. We discuss the same in this addendum.

cs.CR↗

arXiv2025

TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture

This paper presents TT-TFHE, a deep neural network Fully Homomorphic Encryption (FHE) framework that effectively scales Torus FHE (TFHE) usage to tabular and image datasets using a recent family of convolutional neural networks called Truth-Table Neural Networks (TTnet). The proposed framework provides an easy-to-implement, automated TTnet-based design toolbox with an underlying (python-based) open-source Concrete implementation (CPU-based and implementing lookup tables) for inference over encrypted data. Experimental evaluation shows that TT-TFHE greatly outperforms in terms of time and accuracy all Homomorphic Encryption (HE) set-ups on three tabular datasets, all other features being equal. On image datasets such as MNIST and CIFAR-10, we show that TT-TFHE consistently and largely outperforms other TFHE set-ups and is competitive against other HE variants such as BFV or CKKS (while maintaining the same level of 128-bit encryption security guarantees). In addition, our solutions present a very low memory footprint (down to dozens of MBs for MNIST), which is in sharp contrast with other HE set-ups that typically require tens to hundreds of GBs of memory per user (in addition to their communication overheads). This is the first work presenting a fully practical solution of private inference (i.e. a few seconds for inference time and a few dozens MBs of memory) on both tabular datasets and MNIST, that can easily scale to multiple threads and users on server side.

cs.CR↗

arXiv2025

Systematic Evaluation of Randomized Cache Designs against Cache Occupancy

Randomizing the address-to-set mapping and partitioning of the cache has been shown to be an effective mechanism in designing secured caches. Several designs have been proposed on a variety of rationales: (1) randomized design, (2) randomized-and-partitioned design, and (3) psuedo-fully associative design. This work fills in a crucial gap in current literature on randomized caches: currently most randomized cache designs defend only contention-based attacks, and leave out considerations of cache occupancy. We perform a systematic evaluation of 5 randomized cache designs- CEASER, CEASER-S, MIRAGE, Scatter-Cache, and Sass-cache against cache occupancy wrt. both performance as well as security. With respect to performance, we first establish that benchmarking strategies used by contemporary designs are unsuitable for a fair evaluation (because of differing cache configurations, choice of benchmarking suites, additional implementation-specific assumptions). We thus propose a uniform benchmarking strategy, which allows us to perform a fair and comparative analysis across all designs under various replacement policies. Likewise, with respect to security against cache occupancy attacks, we evaluate the cache designs against various threat assumptions: (1) covert channels, (2) process fingerprinting, and (3) AES key recovery (to the best of our knowledge, this work is the first to demonstrate full AES key recovery on a randomized cache design using cache occupancy attack). Our results establish the need to also consider cache occupancy side-channel in randomized cache design considerations.

cs.CR↗

arXiv2024

Deep-Lock: Secure Authorization for Deep Neural Networks

Trained Deep Neural Network (DNN) models are considered valuable Intellectual Properties (IP) in several business models. Prevention of IP theft and unauthorized usage of such DNN models has been raised as of significant concern by industry. In this paper, we address the problem of preventing unauthorized usage of DNN models by proposing a generic and lightweight key-based model-locking scheme, which ensures that a locked model functions correctly only upon applying the correct secret key. The proposed scheme, known as Deep-Lock, utilizes S-Boxes with good security properties to encrypt each parameter of a trained DNN model with secret keys generated from a master key via a key scheduling algorithm. The resulting dense network of encrypted weights is found robust against model fine-tuning attacks. Finally, Deep-Lock does not require any intervention in the structure and training of the DNN models, making it applicable for all existing software and hardware implementations of DNN.

cs.LG↗

arXiv2024

Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM

Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes - passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is important to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean (A2B) conversion. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform.

cs.CR↗

arXiv2023

A short note on the paper `Are Randomized Caches Really Random?'

In this paper, we analyse the results and claims presented in the paper \emph{`Are Randomized Caches Truly Random? Formal Analysis of Randomized Partitioned Caches'}, presented at HPCA conference 2023. In addition, we also analyse the applicability of `Bucket and Ball' analytical model presented in MIRAGE (Usenix Security 2021) for its security estimation. We put forth the fallacies in the original bucket and ball model and discuss its implications. Finally, we demonstrate a cache occupancy attack on MIRAGE with just $10\%$ of total cache capacity and extend the framework to establish a covert channel and a template-based fingerprinting attack.

cs.CR↗

arXiv2020

ExplFrame: Exploiting Page Frame Cache for Fault Analysis of Block Ciphers

Page Frame Cache (PFC) is a purely software cache, present in modern Linux based operating systems (OS), which stores the page frames that are recently being released by the processes running on a particular CPU. In this paper, we show that the page frame cache can be maliciously exploited by an adversary to steer the pages of a victim process to some pre-decided attacker-chosen locations in the memory. We practically demonstrate an end-to-end attack, ExplFrame, where an attacker having only user-level privilege is able to force a victim process's memory pages to vulnerable locations in DRAM and deterministically conduct Rowhammer to induce faults. We further show that these faults can be exploited for extracting the secret key of table-based block cipher implementations. As a case study, we perform a full-key recovery on OpenSSL AES by Rowhammer-induced single bit faults in the T-tables. We propose an improvised fault analysis technique which can exploit any Rowhammer-induced bit-flips in the AES T-tables.

cs.CR↗