arXiv subjects

János Tapolcai

Publications and source records attributed to János Tapolcai.

6 recordsLinked to original sources

arXiv2026

A Tattered Cloak of Invisibility: Measuring Anonymity Loss in Railgun on Ethereum

From a user's perspective, perhaps the most significant difference between traditional banking services and widely used blockchain-based financial systems is that, in the latter, transactions and, either directly or indirectly, account balances and transaction histories are publicly observable. Therefore, a growing number of cryptographic solutions have been proposed to add a privacy layer to such systems. However, the privacy that users actually obtain does not depend solely on the security of the underlying cryptographic protocol: user behavior, transaction amount patterns, and timing decisions can substantially reduce anonymity. In this work, we study behavioral leakage in cryptocurrency mixers, focusing on Railgun on Ethereum. We aim to heuristically estimate the probability that a given deposit and withdrawal transaction belong to the same user. We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph induced by prior public transactions, amount fingerprints that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways. Our results show that even cryptographically strong privacy systems may suffer substantial anonymity loss due to user behavior and transaction patterns. Our five heuristics are able to uniquely link 17.65% of Railgun withdraw transactions to deposit transactions. We also applied a knapsack solver algorithm that was able to produce a 3.42 bit median anonymity loss for withdraw transactions. This work contributes to a better understanding of the practical privacy limits of mixers and anonymity pools, and points toward safer usage practices and design principles.

cs.CR↗

arXiv2026

Beyond Journals: Rethinking Research Evaluation in Hungarian Computer Science

This study examines the role of top-tier conference publications in Hungarian computer science research. We show that the national scientometric practice, which is currently journal-oriented, diverges from international norms, creating incentive distortions in researcher evaluation. By linking multiple databases (iCore, DBLP, MTMT, MTA-ATT), we mapped Hungarian-affiliated CORE A* and A conference papers, their temporal and thematic distribution, and author trajectories. Our results indicate that, in theoretical fields, publishing at international conferences became common earlier than in applied fields. At the same time, in applied fields, successful researchers are more likely to continue their careers in foreign institutions or in industry positions. Overall, a substantial share of the already established, internationally most successful researchers are now affiliated with institutions abroad. We recommend recognizing CORE A* papers as equivalent to D1 and CORE A papers as equivalent to Q1 journals in national evaluation systems.

cs.DL↗

arXiv2026

Where Does MEV Really Come From? Revisiting CEXDEX Arbitrage on Ethereum

A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV)revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX-DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance - though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX-DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black-Scholes (BS) stochastic differential equation (SDE) - i.e., geometric Brownian motion - and assumed continuous price trajectories where asset prices move in small increments only.We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.

cs.CR↗

arXiv2017

Digital Encyclopedia of Scientific Results

This study describes a vision, how technology can help improving the efficiency in research. We propose a new clean-slate design, where more emphasis is given on the correctness and up-to-dateness of the scientific results, it is more open to new ideas and better utilize the research efforts worldwide by providing personalized interface for every researcher. The key idea is to reveal the structure and connections of the problems solved in the scientific studies. We will build the system with the main focus on the solved problems itself, and treat the studies only as one presentation form. By utilizing artificial intelligence and machine learning on the network of the solved problems we could coordinate individual research activities in a large scale, that has never been seen before.

cs.DL↗

arXiv2014

Compressing IP Forwarding Tables: Towards Entropy Bounds and Beyond

Lately, there has been an upsurge of interest in compressed data structures, aiming to pack ever larger quantities of information into constrained memory without sacrificing the efficiency of standard operations, like random access, search, or update. The main goal of this paper is to demonstrate how data compression can benefit the networking community, by showing how to squeeze the IP Forwarding Information Base (FIB), the giant table consulted by IP routers to make forwarding decisions, into information-theoretical entropy bounds, with essentially zero cost on longest prefix match and FIB update. First, we adopt the state-of-the-art in compressed data structures, yielding a static entropy-compressed FIB representation with asymptotically optimal lookup. Then, we re-design the venerable prefix tree, used commonly for IP lookup for at least 20 years in IP routers, to also admit entropy bounds and support lookup in optimal time and update in nearly optimal time. Evaluations on a Linux kernel prototype indicate that our compressors encode a FIB comprising more than 440K prefixes to just about 100--400 KBytes of memory, with a threefold increase in lookup throughput and no penalty on FIB updates. This technical report contains a number of important corrections and revisions to the original manuscript.

cs.DS↗

arXiv2013

Memory size bounds of prefix DAGs

In this report an entropy bound on the memory size is given for a compression method of leaf-labeled trees. The compression converts the tree into a Directed Acyclic Graph (DAG) by merging isomorphic subtrees.

cs.DS↗