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Ekleen Kaur

Publications and source records attributed to Ekleen Kaur.

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Fractional Verkle Trees: A Hypertree Decomposition and Verified Proof Serialization Architecture for High-Performance Blockchain State Accumulators

Modern blockchain state management faces a critical scalability bottleneck: maintaining cryptographic commitments over hundreds of millions of entries becomes computationally prohibitive. Ethereum's transition to Verkle Trees: polynomial commitment accumulators reducing proof sizes from O(width * depth) to O(depth) via constant-size IPA vector commitments, is a critical step toward stateless operation. Yet, current implementations exhibit pathological characteristics that burden home validators. We identify four inefficiencies in the reference go-verkle implementation \cite{kaur2025goverkle, kaur2025goethereum}: (1) phantom node creation during non-existent account deletion; (2) 64-byte database keys triggering excessive LSM-tree compaction; (3) redundant memory copying in proof deserialization; (4) a Proof of Absence wire format incompatibility causing non-deterministic serialization. We present Fractional Verkle Trees (FVT), a hypertree decomposition partitioning global state into N independent sub-accumulators coordinated by a Merkle commitment tree, achieving improved cache locality, zero-lock-contention goroutine-parallel commitment computation, and faster root recomputation (91 $\mu$s vs $\sim$500 ms). We address each inefficiency via existence checks, 32-byte SHA256 node references, zero-copy reference-counted buffers, and HashMap-based lexicographic deduplication. Benchmarks on Apple M1 Pro show 57\% heap allocation reduction (566,760 to 242,004 bytes per 10K proofs), parallel insertion at 2,433 ns/op, and network-wide elimination of 4.85 PB/year across 6,000 full nodes, advancing the Ethereum stateless roadmap.

cs.CR

Layer 2 Blockchains Simplified: A Survey of Vector Commitment Schemes, ZKP Frameworks, Layer-2 Data Structures and Verkle Trees

Layer-2 (L2) protocols address the fundamental limitations of Layer-1 (L1) blockchains by offloading computation while anchoring trust to the parent chain. This architectural shift, while boosting throughput, introduces a new, complex security surface defined by off-chain components like sequencers, bridges, and data availability mechanisms. Prior literature[31][33] offers fragmented views of this risk. This paper presents the first unified, security-focused survey that rigorously maps L2 architecture to its underlying cryptographic security. We dissect the technical progression from L1 primitives to the core of modern L2s, analyzing the security assumptions(Discrete Logarithm, Computational Diffie-Hellman, Bilinear Diffie-Hellman) of ZK frameworks (Groth16, Plonk) and their corresponding commitment schemes (KZG, IPA). We formalize a comprehensive L2 threat model encompassing sequencer liveness, bridge exploits, and data-availability failures. This work serves as an accessible yet rigorous reference for researchers and developers to reason about L2 security from a deep crypto-mathematical perspective.

cs.CR

The Limits of Conditional Volatility: Assessing Cryptocurrency VaR under EWMA and IGARCH Models

The application of the standard static Geometric Brownian Motion (GBM) model for cryptocurrency risk management resulted in a systemic failure, evidenced by a 80.67% chance of loss in the 5% value-at-risk benchmark. This study addresses a critical literature gap by comparatively testing three conditional volatility models the EWMA/IGARCH baseline, an IGARCH model augmented with explicit mean reversion (IGARCH + MR), and a modified EGARCH-style asymmetric shock model within a correlated Monte Carlo VaR framework. Crucially, the analysis is applied specifically to high-beta altcoins (XRP, SOL, ADA), an asset class largely neglected by mainstream GARCH literature. Our results demonstrate that imposing stationarity (IGARCH + MR) drastically underestimates downside risk (5 percent value-at-risk reduced by 50%), while the asymmetric model (Model 3) leads to severe over-penalization. The EWMA/IGARCH baseline, characterized by infinite volatility persistence (alpha + beta = 1), provided the only robust conditional volatility estimate. This finding constitutes a formal rejection of the conventional financial hypotheses of volatility mean reversion and the asymmetric leverage effect in the altcoin asset class, establishing that non-stationary frameworks are a prerequisite for regulatory-grade risk modeling in this domain.

cs.CR

The Limits of Lognormal: Assessing Cryptocurrency Volatility and VaR using Geometric Brownian Motion

The integration of cryptocurrencies into institutional portfolios necessitates the adoption of robust risk modeling frameworks. This study is a part of a series of subsequent works to fine-tune model risk analysis for cryptocurrencies. Through this first research work, we establish a foundational benchmark by applying the traditional industry-standard Geometric Brownian Motion (GBM) model. Popularly used for non-crypto financial assets, GBM assumes Lognormal return distributions for a multi-asset cryptocurrency portfolio (XRP, SOL, ADA). This work utilizes Maximum Likelihood Estimation and a correlated Monte Carlo Simulation incorporating the Cholesky decomposition of historical covariance. We present our stock portfolio model as a Minimum Variance Portfolio (MVP). We observe the model's structural shift within the heavy-tailed, non-Gaussian cryptocurrency environment. The results reveal limitations of the Lognormal assumption: the calculated Value-at-Risk at the 5% confidence level over the one-year horizon. For baselining our results, we also present a holistic comparative analysis with an equity portfolio (AAPL, TSLA, NVDA), demonstrating a significantly lower failure rate. This performance provides conclusive evidence that the GBM model is fundamentally the perfect benchmark for our subsequent works. Results from this novel work will be an indicator for the success criteria in our future model for crypto risk management, rigorously motivating the development and application of advanced models.

q-fin.RM

Time Transitive Functions for Zero Knowledge Proofs

Verifiable delay functions have found a lot of applications in blockchain technology in recent times. Continuous verifiable delay functions are an improvement over the basic notion of VDFs with recursive capabilities. We are proposing the application of VDF for constructing more space time-efficient provers and simulators required for the iterative non-interactive zero-knowledge systems.

cs.CR