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Daksh Pandey

Publications and source records attributed to Daksh Pandey.

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Cryptanalysis of the Legendre Pseudorandom Function over Extension Fields

The Legendre Pseudorandom Function (PRF) is a highly efficient cryptographic primitive built upon the Legendre symbol, valued for its low multiplicative complexity in Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) protocols. While its security over prime fields $\mathbb{F}_p$ is well-documented, recent interest has shifted toward instantiations over extension fields $\mathbb{F}_{p^r}$. This paper presents the first comprehensive cryptanalysis of the single-degree Legendre PRF operating over $\mathbb{F}_{p^r}$. First, we analyze polynomial input encoding under a standard passive threat model (sequential additive counter queries). We demonstrate that while the absence of polynomial carry-overs causes an asynchronous "no-carry fracture" that neutralizes classical sliding-window collision attacks, the fracture itself is deterministically periodic. By introducing a novel "Differential Signature" bucketing technique, we prove that an adversary can systematically group fractured sequences by their structural shapes to bypass this defense, recovering the secret key in $\mathcal{O}(U \cdot p^r/M)$ operations, where $U$ is the unicity distance. Second, we evaluate the PRF under an active Chosen-Query threat model. We demonstrate that an adversary can circumvent the additive fracture by evaluating the PRF along a geometric sequence generated by a primitive polynomial. This structure invokes strict multiplicative homomorphism over $\mathbb{F}^*_{p^r}$, permitting a direct generalization of state-of-the-art table collision attacks to extract the key in $\mathcal{O}(p^r/M)$ operations. Finally, we establish the cryptographic boundaries of these attacks, formally proving the necessity of higher-degree key variants ($d \ge 2$) to achieve exponential security against structural reduction in extension fields.

cs.CR

Emergent Dark Patterns in AI-Generated User Interfaces

The advancement of artificial intelligence has transformed user interface design by enabling adaptive and personalized systems. Alongside these benefits, AI driven interfaces have also enabled the emergence of dark patterns, which are manipulative design strategies that influence user behavior for financial or business gain. As AI systems learn from data that already contains deceptive practices, they can replicate and optimize these patterns in increasingly subtle and personalized ways. This paper examines AI generated dark patterns, their psychological foundations, technical mechanisms, and regulatory implications in India. We introduce DarkPatternDetector, an automated system that crawls and analyzes websites to detect dark patterns using a combination of UI heuristics, natural language processing, and temporal behavioral signals. The system is evaluated on a curated dataset of dark and benign webpages and achieves strong precision and recall. By aligning detection results with India's Digital Personal Data Protection Act, 2023, this work provides a technical and regulatory framework for identifying and mitigating deceptive interface practices. The goal is to support ethical AI design, regulatory enforcement, and greater transparency in modern digital systems.

cs.HC

Polynomial Contrastive Learning for Privacy-Preserving Representation Learning on Graphs

Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations on graph data without requiring manual labels. However, leading SSL methods like GRACE are fundamentally incompatible with privacy-preserving technologies such as Homomorphic Encryption (HE) due to their reliance on non-polynomial operations. This paper introduces Poly-GRACE, a novel framework for HE-compatible self-supervised learning on graphs. Our approach consists of a fully polynomial-friendly Graph Convolutional Network (GCN) encoder and a novel, polynomial-based contrastive loss function. Through experiments on three benchmark datasets -- Cora, CiteSeer, and PubMed -- we demonstrate that Poly-GRACE not only enables private pre-training but also achieves performance that is highly competitive with, and in the case of CiteSeer, superior to the standard non-private baseline. Our work represents a significant step towards practical and high-performance privacy-preserving graph representation learning.

cs.LG