Subhamoy Maitra

Purushottam Saha, Avirup Chakraborty, Sourish Sarkar et al.

The 13-card variant of Classic Indian Rummy is a sequential game of incomplete information that requires probabilistic reasoning and combinatorial decision-making. This paper proposes a rule-based framework for strategic play, driven by a new hand-evaluation metric termed MinDist. The metric modifies the MinScore metric by quantifying the edit distance between a hand and the nearest valid configuration, thereby capturing structural proximity to completion. We design a computationally efficient algorithm derived from the MinScore algorithm, leveraging dynamic pruning and pattern caching to exactly calculate this metric during play. Opponent hand-modeling is also incorporated within a two-player zero-sum simulation framework, and the resulting strategies are evaluated using statistical hypothesis testing. Empirical results show significant improvement in win rates for MinDist-based agents over traditional heuristics, providing a formal and interpretable step toward algorithmic Rummy strategy design.

Anh Tu Ngo, Anupam Chattopadhyay, Subhamoy Maitra

In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as well as in presenting the defenses as well. On the attack side, a carefully planted cryptographic backdoor enables powerful and invisible attack on the NN. Considering the defense, we present applications: first, a provably robust NN watermarking scheme; second, a protocol for guaranteeing user authentication; and third, a protocol for tracking unauthorized sharing of the NN intellectual property (IP). From a broader theoretical perspective, borrowing the ideas from Goldwasser et. al. [FOCS 2022], our main contribution is to show that all these instantiated practical protocol implementations are provably robust. The protocols for watermarking, authentication and IP tracking resist an adversary with black-box access to the NN, whereas the backdoor-enabled adversarial attack is impossible to prevent under the standard assumptions. While the theoretical tools used for our attack is mostly in line with the Goldwasser et. al. ideas, the proofs related to the defense need further studies. Finally, all these protocols are implemented on state-of-the-art NN architectures with empirical results corroborating the theoretical claims. Further, one can utilize post-quantum primitives for implementing the cryptographic backdoors, laying out foundations for quantum-era applications in machine learning (ML).

Subhamoy Maitra, Chandra Sekhar Mukherjee, Pantelimon Stanica et al.

Boolean functions are important primitives in different domains of cryptology, complexity and coding theory. In this paper, we connect the tools from cryptology and complexity theory in the domain of Boolean functions with low polynomial degree and high sensitivity. It is well known that the polynomial degree of of a Boolean function and its resiliency are directly connected. Using this connection we analyze the polynomial degree-sensitivity values through the lens of resiliency, demonstrating existence and non-existence results of functions with low polynomial degree and high sensitivity on small number of variables (upto 10). In this process, borrowing an idea from complexity theory, we show that one can implement resilient Boolean functions on a large number of variables with linear size and logarithmic depth. Finally, we extend the notion of sensitivity to higher order and note that the existing construction idea of Nisan and Szegedy (1994) can provide only constant higher order sensitivity when aiming for polynomial degree of $n-ω(1)$. In this direction, we present a construction with low ($n-ω(1)$) polynomial degree and super-constant $ω(1)$ order sensitivity exploiting Maiorana-McFarland constructions, that we borrow from construction of resilient functions. The questions we raise identify novel combinatorial problems in the domain of Boolean functions.

Jaydip Sen, Subhamoy Maitra

In-network data aggregation in Wireless Sensor Networks (WSNs) provides efficient bandwidth utilization and energy-efficient computing.Supporting efficient in-network data aggregation while preserving the privacy of the data of individual sensor nodes has emerged as an important requirement in numerous WSN applications. For privacy-preserving data aggregation in WSNs, He et al. (INFOCOM 2007) have proposed a Cluster-based Private Data Aggregation (CPDA) that uses a clustering protocol and a well-known key distribution scheme for computing an additive aggregation function in a privacy-preserving manner. In spite of the wide popularity of CPDA, it has been observed that the protocol is not secure and it is also possible to enhance its efficiency. In this paper, we first identify a security vulnerability in the existing CPDA scheme, wherein we show how a malicious participant node can launch an attack on the privacy protocol so as to get access to the private data of its neighboring sensor nodes. Next it is shown how the existing CPDA scheme can be made more efficient by suitable modification of the protocol. Further, suitable modifications in the existing protocol have been proposed so as to plug the vulnerability of the protocol.