Debdeep Mukhopadhyay

• Professor (Full) at Indian Institute of Technology Kharagpur

Smart meters provide fine-grained power usage profiles of consumers to various utility providers, thus facilitating multiple grid functionalities such as load monitoring, real-time pricing, demand response, etc. However, information leakage from such usage profiles reveals consumers’ private day-to-day life patterns and their home presence/absence,...

As computer systems become more complex, evaluating performance requires tracking various hardware performance counters that capture the system's internal activities. While these counters provide valuable insights, their growing number makes it challenging to identify the most relevant ones for performance analysis. In this paper, we investigate th...

Machine learning, with its myriad applications, has become an integral component of numerous AI systems. A common practice in this domain is the use of transfer learning, where a pre-trained model’s architecture, readily available to the public, is fine-tuned to suit specific tasks. As Machine Learning as a Service (MLaaS) platforms increasingly us...

Adversarial examples make Deep Learning (DL) models vulnerable to safe deployment in practical systems. Although several techniques have been proposed in the literature, defending against adversarial attacks is still challenging. The current work identifies weaknesses of traditional strategies in detecting and classifying adversarial examples. To o...

Static random access memory (SRAM)-based physically unclonable functions (PUFs) utilize unpredictable start-up values (SUVs) for key generation, making them widely adopted in cryptographic systems. This unpredictability in SUVs is accompanied by device noise that escalates with process-voltage–temperature (PVT) variations, resulting in significant...

NIST’s recent review of the widely employed special publication (SP) 800–22 randomness testing suite has underscored several shortcomings, particularly the absence of entropy source modeling and the necessity for large sequence lengths. Motivated by this revelation, we explore low-dimensional modeling of the entropy source in random number generato...

Transient execution attacks have been one of the widely explored microarchitectural side channels since the discovery of Spectre and Meltdown. However, much of the research has been driven by manual discovery of new transient paths through well-known speculative events. Although a few attempts exist in literature on automating transient leakage dis...

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 co...

Smart meters provide fine-grained power usage profiles of consumers to utility providers to facilitate various grid functionalities such as load monitoring, real-time pricing, etc. However, information leakage from these usage profiles can potentially reveal sensitive aspects of consumers’ daily routines and their home absence, as state-of-the-art...

We propose the first distributed version of a simple, efficient, and provably quantum-safe pseudorandom function (PRF). The distributed PRF (DPRF) supports arbitrary threshold access structures based on the hardness of the well-studied Learning with Rounding (LWR) problem. Our construction (abbreviated as \(\textsf{PQDPRF}\)) practically outperform...

This brief editorial gives a short, two-page overview of the ASHES 2021 workshop. It shall serve as an introduction for this special issue at JCEN.

Remote attestation is a request-response based security service that permits a trusted entity (verifier) to check the current state of an untrusted remote device (prover). The verifier initiates the attestation process by sending an attestation challenge to the prover; the prover responds with its current state, which establishes its trustworthines...

Searchable symmetric encryption (SSE) is a special class of encryption schemes for computing directly over encrypted data. SSE aims to be significantly more efficient as compared to other solutions, such as fully homomorphic encryption (FHE), while leaking only minimal information to the adversary. SSE is particularly efficient and scalable for Boo...

The development of Artificial Intelligence (AI) based systems to automatically generate hardware systems has gained an impulse that aims to accelerate the hardware design cycle with no human intervention. Recently, the striking AI-based system ChatGPT from OpenAI has achieved a momentous headline and has gone viral within a short span of time since...

Generic vulnerability assessment of cipher implementations against Fault Attacks (FA) is a largely unexplored research area. Security assessment against FA is critical for FA countermeasures. On several occasions, countermeasures fail to fulfil their sole purpose of preventing FA due to flawed design or implementation. This paper proposes a generic...

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 est...

While recent advancements of Deep Learning (DL) in solving complex real-world tasks have spurred their popularity, the usage of privacy-rich data for their training in varied applications has made them an overly-exposed threat surface for privacy violations. Moreover, the rapid adoption of cloud-based Machine-Learning-asa-Service (MLaaS) has broade...

ARM TrustZone is a system-on-chip security solution that provides hardware guarantees to isolate the untrusted applications running in the normal world from sensitive computation and data by placing them in the secure world. In a multi-tenant scenario, such isolation is paramount to protect tenants from each other and is guaranteed by partitioning...

Searchable Symmetric Encryption (SSE) supports efficient yet secure query processing over outsourced symmetrically encrypted databases without the need for decryption. A longstanding open question has been the following: can we design a fast, scalable, linear storage and low-leakage SSE scheme that efficiently supports arbitrary Boolean queries ove...

Commitment schemes are one of the basic building blocks to construct secure protocols for multi party computation. Many recent works are exploring hardware primitives like physically unclonable functions to build keyless cryptographic protocols, with minimal assumptions. The asymmetric nature and non-invertibility property of PUFs are widely exploi...

Besides enormous research efforts in the design of Physically Unclonable Functions (PUFs), its vulnerabilities are still being exploited using machine learning (ML) based model-building attacks. Due to inherent complicacy in exploring and manually converging to a strong PUF composition, the challenge of building ML-attack resistant PUFs continues....

In side-channel analysis (SCA), Points-of-Interest (PoIs), i.e., the informative sample points remain sparsely scattered across the whole side-channel trace. Several works in the SCA literature have demonstrated that the attack efficacy could be significantly improved by combining information from the sparsely occurring PoIs. In Deep Learning (DL),...

Physically Unclonable Functions (PUFs) with large challenge space (also called Strong PUFs) are promoted for usage in authentications and various other cryptographic and security applications. In order to qualify for these cryptographic applications, the Boolean functions realized by PUFs need to possess a high nonlinearity (NL). However, with a la...

Power side-channels give rise to several potent attack vectors for leaking information in digital circuits. While a plethora of (mathematically robust) solutions exist to tackle such side-channels, their deployment through existing VLSI design-flows remains an important engineering issue. Besides, most existing solutions result in significant hardw...

Smart metering is a mechanism through which fine-grained power consumption profiles of the consumers are collected periodically in a Smart grid. However, a growing concern in this regard is that the leakage of consumers’ consumption data may reveal their daily life patterns as the state-of-the-art metering strategies lack adequate security and priv...

The widespread advent of the Internet-of-Things has motivated new design strategies for lightweight block ciphers. In particular, security against traditional cryptanalysis should ideally be complemented by resistance to side-channel attacks, while adhering to low area and power requirements. In FSE 2018, Ghoshal et al. proposed a dedicated design...

Modern vehicles contain a multitude of electronic control units (ECUs) that implement software features controlling most of the operational, entertainment, connectivity, and safety aspects of the vehicle. However, with security requirements often being an afterthought in automotive software development, incorporation of such software features with...

Homomorphic encryption (HE) is the ultimate tool for performing secure computations even in untrusted environments. Application of HE for deep learning (DL) inference is an active area of research, given the fact that DL models are often deployed in untrusted environments (e.g., third-party servers) yet inferring on private data. However, existing...

This paper initiates the study of “Cryptophasia in Hardware” – a phenomenon that allows hardware circuits/devices with no pre-established secret keys to securely exchange secret information over insecure communication networks. The study of cryptophasia is motivated by the need to establish secure communication channels between lightweight resource...

Deep learning (DL) has revolutionized Side Channel Analysis (SCA) in recent years. One of the major advantages of DL in the context of SCA is that it can automatically handle masking and desynchronization countermeasures, even while they are applied simultaneously for a cryptographic implementation. However, the success of the attack strongly depen...

The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to their ever-improving performance to solve challenging tasks. Despite overwhelming promises, the deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human ey...

Physically Unclonable Functions (PUFs) have been a potent choice for enabling low-cost, secure communication. However, the state-of-the-art strong PUFs generate single-bit response. So, we propose PUF-COTE: a high throughput architecture based on linear feedback shift register and a strong PUF as the "base"-PUF. At the same time, we obfuscate the c...

Recent Deep Learning (DL) advancements in solving complex real-world tasks have led to its widespread adoption in practical applications. However, this opportunity comes with significant underlying risks, as many of these models rely on privacy-sensitive data for training in a variety of applications, making them an overly-exposed threat surface fo...

The prevalent usage and unparalleled recent success of Deep Neural Network (DNN) applications have raised the concern of protecting their Intellectual Property (IP) rights in different business models to prevent the theft of trade secrets. In this article, we propose a lightweight, generic, key-based DNN IP protection methodology, NN-Lock , to defe...

In recent years, connected and intelligent vehicles have posed advanced risks to road safety and vehicle thefts. The keyless entry and immobiliser systems of luxury vehicles have been under extensive scrutiny and found to be vulnerable against lack of mutual authentication in challenge-response protocol, smaller key size for the cipher, amplificati...

The globalization of the integrated circuit (IC) manufacturing industry has lured the adversary to come up with numerous malicious activities in the IC supply chain. Logic locking has risen to prominence as a proactive defense strategy against such threats. CAS-Lock (proposed in CHES'20), is an advanced logic locking technique that harnesses the co...

Interpose PUF (iPUF) is a strong PUF construction that was shown to be vulnerable against empirical machine learning as well as PAC learning attacks. In this work, we extend the PAC Learning results of Interpose PUF to prove that the variants of iPUF are also learnable in the PAC model under the Linear Threshold Function representation class.

Logic obfuscation has evolved as a promising countermeasure against IP piracy. The Finite State Machine (FSM) is often obfuscated in a sequential circuit using suitable strategies. One such strategy proposed to obfuscate each state transition of the FSM using a class of non-group additive cellular automata (CA) called \(D1\,*\,CA\) and \(D1\,*\,CA_...

Physically Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) are two highly useful hardware primitives to build up the root-of-trust for embedded devices in Internet-of-Things and Cyber-Physical System applications. These applications demand the primitives be lightweight, yet flexible. However, PUFs are designed to offer repeti...

The security of Deep Learning classifiers is a critical field of study because of the existence of adversarial attacks. Such attacks usually rely on the principle of transferability, where an adversarial example crafted on a surrogate classifier tends to mislead the target classifier trained on the same dataset even if both classifiers have quite d...

Protection against Side-Channel (SCA) and Fault Attacks (FA) requires two classes of countermeasures to be simultaneously embedded in a cryptographic implementation. It has already been shown that a straightforward combination of SCA and FA countermeasures are vulnerable against FAs, such as Statistical Ineffective Fault Analysis (SIFA) and Fault T...

Statistical Ineffective Fault Analysis (SIFA) and Fault Template Attack (FTA) are two recently proposed classes of Fault Attacks (FA), which evade almost all existing FA countermeasures, even while they are combined with Side-Channel Analysis (SCA) countermeasures such as masking. Protecting against these attacks requires an entirely new class of m...

Formally bounding side-channel leakage is important to bridge the gap between theory and practice in cryptography. However, bounding side-channel leakages is difficult because leakage in a cryptosystem could be from several sources. Moreover, the amount of leakage from a source may vary depending on the implementation of the cipher and the form of...

The Rowhammer bug has exposed a severe reliability issue in modern commodity-grade DRAM modules where repeated accesses to a particular row can cause bit-flips in its adjacent rows. It is a prime example where a reliability issue can lead to a practical security vulnerability that can aid an adversary to mount an array of local and remote attacks t...

In recent years, the transitioning of conventional power grid system into the smart grid infrastructure has made the power distribution network more susceptible towards faults and physical attacks. In this context, we discuss recently proposed Manipulation-of-Demand via IoT attack, False Data Injection Attacks and Electric Fault Attacks. These atta...

In this work, we prove that Multiplexer PUF (MPUF) and SN-PUF are learnable in the PAC model. First, we show that both the designs can be represented as a function of Linear Threshold Functions. We show that the noise sensitivity of (n, k)-MPUF and SN-PUF can be bounded by O(2 k √) and O(N √) respectively. Finally, we show that as a result of bound...

Hardware security circuits based on Physically Unclonable Functions (PUF) are finding widespread use due to increasing adoption of IoT devices. However, existing strong PUFs such as Arbiter-PUF (APUF) and its compositions are susceptible to machine learning (ML) attacks due to a linear relationship between the challenge and its response. In this pa...

Abstract Deep learning has evolved as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. The advancement of deep learning has been so radical that today it can surpass human‐level performance. As a conseque...

Microarchitectural attacks on computing systems often stem from simple artefacts in the underlying architecture. In this paper, we focus on the Return Address Stack (RAS), a small hardware stack present in modern processors to reduce the branch miss penalty by storing the return addresses of each function call. The RAS is useful to handle specifica...

Pairing cryptosystems are powerful mathematical tools for the development of cryptographic protocols that provide end-to-end security for applications like Internet-of-Things (IoT), cloud services and cyber-physical systems (CPS). However, these applications require light-weight implementations but still real-time and flexible. The flexibility can...

Micro-architectural side-channel attacks are major threats to the most mathematically sophisticated encryption algorithms. In spite of the fact that there exist several defense techniques, the overhead of implementing the countermeasures remains a matter of concern. A promising strategy is to develop online detection and prevention methods for thes...

In logic locking, the finite-state machine (FSM) embedded in a sequential circuit is often chosen to be obfuscated. Such an obfuscation scheme using a class of nongroup additive cellular automata (CA) called $D1 * CA$ and $D1 * CA_{\mathrm{ dual}}$ to obfuscate each state transition of an FSM has been proposed previously. Since $D1 * CA$ and...

Field-programmable gate arrays (FPGAs) have gained massive popularity today as accelerators for a variety of workloads, including big data analytics, and parallel and distributed computing. This has fueled the study of mechanisms to provision FPGAs among multiple tenants as general purpose computing resources on the cloud. Such mechanisms offer new...

SKINNY is a family of tweakable lightweight block ciphers, proposed in CRYPTO 2016. The proposal of SKINNY describes two block size variants of 64 and 128 bits as well as three options for tweakey. In this paper, we present fault attacks (FA) on all SKINNY variants. In the first part of the paper, we propose differential fault analysis (DFA) attack...