Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.06268

Beyond Identity Creation: Bilinear Sybil Cost in Identities and Time

Homayoun Maleki·Nekane Sainz·Jon Legarda·Igor Santos-Grueiro
View original source ↗Read source document ↗Export metadata ↓

Abstract

Sybil defenses price the creation of an identity, yet influence goes, again and again, to active identities. We study the cost of maintaining influence. A defense has a bilinear Sybil cost with ratio $A$ if keeping $s$ identities active for $T$ periods costs at least $sT/A$ honest units: the average cost per identity-window is at least $1/A$ times an honest participant's cost. Recurrence alone does not guarantee this cost. The cost can be carried over from earlier periods, shared among many identities, or be negligible when fast hardware does the work of thousands of honest devices. We give four sufficient conditions for a defense that checks every identity each period to achieve bilinear Sybil cost: identity binding, freshness, a shared deadline, and sequential, non-batchable work. They yield two guarantees: a bilinear total cost, with $A$ the speed advantage of the adversary's fastest hardware, and, because the work must be fresh and finished by a common deadline, a bound on the identities active in a period by the processors running at that time. We prove that none of the four follows from the other three. A verifiable delay function on an identity-bound input meets all four without payments, with $A$ of 2 to 8 for CPUs and about 28 for a hypothetical ASIC, against $10^3$ to $10^7$ for hash puzzles. We measure one active identity per core per period against about 80,000 under a signed heartbeat, no gain from batching up to 1,000 identities, and rejection of all tested attacks.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Homayoun Maleki, Nekane Sainz, Jon Legarda, Igor Santos-Grueiro. 2026-11-04. Beyond Identity Creation: Bilinear Sybil Cost in Identities and Time. https://arxiv.org/abs/2611.06268

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

How to Backdoor Image Knowledge Distillation

Knowledge distillation is widely used to transfer behavior from a large teacher model to a smaller student. It is often assumed to be safe when the teacher is clean, because classic backdoor attacks rely on poisoned labels and triggers in supervised training, whereas distillation trains the student to match a teacher's outputs. We show that this assumption can fail when the distillation dataset itself is poisoned. Our attack injects triggered and manipulated images that a clean teacher already predicts as an attacker chosen target label, which causes the student to learn a backdoor even though the teacher remains unaffected. We evaluate this threat across multiple manipulation strategies, including targeted adversarial perturbations and targeted GAN based class transitions, and study how distillation settings influence both accuracy and attack success. We show that the attack remains effective even at a 10% poisoning rates. The results demonstrate that a clean teacher alone is not a sufficient safeguard: poisoned distillation data can produce a strongly backdoored student while maintaining competitive performance on clean images. These findings show that the integrity and provenance of distillation data are part of the security boundary of data intensive KD pipelines, even when the teacher itself is trusted.

cs.CR↗
arXiv2026

Cryptanalysis of Isogeny-Based Quantum Money with Rational Points

Quantum money is a representative application in cryptography that utilizes quantum technology to achieve security that is unattainable with classical systems. Since the theoretical proposal by Wiesner in 1983, the most recent instantiation is the quantum money using class group actions on elliptic curves, proposed by Montgomery and Sharif in Asiacrypt'24. In this work, we propose a concrete cryptanalysis by leveraging the efficiency of evaluating division polynomials with the coordinates of rational points, offering a speedup of $O(\log^4p)$ compared to the brute-force attack. While the resulting attack still runs in exponential time, our method provides the first nontrivial algorithmic insight into their structural hardness. Furthermore, due to the algebraic properties of rational points and quadratic twists, our technique naturally yields a more efficient verification procedure for the underlying quantum money. We believe this new line of analysis opens promising directions for future research in elliptic-curve-based quantum cryptography.

cs.CR↗
arXiv2026

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings

Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, which is limited in cybersecurity due to privacy and data-sharing restrictions. To address this challenge, Federated Learning (FL) has recently emerged as a novel paradigm that enables collaborative training of ML models across multiple clients while ensuring that sensitive data remains local. Nevertheless, Federated Averaging (FedAvg), the canonical FL algorithm, has shown poor convergence in heterogeneous environments characterised by non-independent and identically distributed (i.i.d.) data distributions and unbalanced dataset sizes across clients, conditions frequently observed in cybersecurity contexts. To overcome these challenges, several alternative FL algorithms have been developed, yet their applicability to network intrusion detection remains insufficiently explored. This study systematically evaluates a range of FL algorithms in the context of network intrusion detection for DDoS attacks. Using a dataset of recent network attacks, the evaluation considers detection performance, training time, and communication overhead under non-i.i.d. settings, providing practical insights into the selection of FL solutions for network intrusion detection.

cs.CR↗