Analysis of Information Leakage in Fuzzy Matchers for Biometric Security Systems

Keskeiset käsitteet

The author explores the vulnerabilities of fuzzy matchers in biometric systems due to information leakage, focusing on distance evaluation and error correction mechanisms.

Tiivistelmä

The content delves into the risks posed by information leakage in fuzzy matchers used in biometric security systems. It covers various scenarios, attacks, and complexities associated with leaking distance, error positions, and values below and above the threshold. The study provides insights into potential security risks and privacy concerns in practical applications.

Tilastot

"The complexity of this attack is O(qn−ε + qε) queries to σx,ε."
"The complexity of the attack for recovering x is O(qn−ε + q) queries to σx,ε."
"The complexity of the attack is O(qn−ε) queries to σx,ε."
"The complexity of the attack for recovering x is O(nq) queries to σx,ε."
"Hence, the complexity of the attack for recovering x is O(q)."
"As each coordinate has q possible values and there are n coordinates, this is done in O(nq) steps."
"In this case, d(x, y) the distance between y ∈ Znq the fresh input and x ∈ Znq the old input is leaked to the attacker regardless of the threshold."
"Given ε a threshold, x ∈ Znq a vector and Ξ a fuzzy matcher such that Ξ.Match leaks the positions of errors below the threshold, an attacker can retrieve x in O(q) queries to σx,ε."

Lainaukset

"The provided security analyses are currently reaching their limits."
"Further research involves refining the accumulation attack as suggested above and exploring other distance metrics."
"The attacks exploiting information both below and above significantly decrease security."

Tärkeimmät oivallukset

On the Leakage of Fuzzy Matchers

by Axel Durbet,... klo arxiv.org 03-01-2024

https://arxiv.org/pdf/2307.13717.pdf

Syvällisempiä Kysymyksiä

How do side-channel attacks impact homomorphic encryption?

Side-channel attacks can significantly impact homomorphic encryption by exploiting unintended information leakage through side channels such as power consumption, timing variations, or electromagnetic emanations. These attacks can reveal sensitive data or cryptographic keys, compromising the security of the system. In the context of homomorphic encryption, side-channel attacks can target the intermediate computations performed during the encryption and decryption processes, potentially revealing confidential information and undermining the confidentiality guarantees provided by homomorphic encryption schemes.

What are potential improvements for exhaustive search attacks beyond theoretical techniques?

Beyond theoretical techniques, potential improvements for exhaustive search attacks involve optimizing the search process to reduce computational complexity and improve efficiency. One approach is to leverage parallel processing capabilities to speed up the search process by distributing computations across multiple processors or cores. Additionally, implementing intelligent algorithms that prioritize certain branches of the search tree based on heuristics or probabilistic methods can help narrow down possible solutions more effectively. Furthermore, utilizing specialized hardware accelerators or GPUs tailored for specific types of computations involved in exhaustive searches can enhance performance and reduce overall execution time.

How does skewed distribution of errors affect accumulation attacks?

A skewed distribution of errors in accumulation attacks impacts their effectiveness and efficiency. When errors are not uniformly distributed but exhibit a skewed pattern with certain error patterns occurring more frequently than others, it complicates the recovery process for an attacker conducting an accumulation attack. In such scenarios, where some errors are more likely to occur than others during authentication sessions, legitimate users may need to undergo a higher number of authentications before all bits of their hidden input are revealed due to this non-uniform distribution pattern. This increases both the expected number of observations required from genuine clients and introduces variability in how quickly attackers can accumulate complete knowledge about a target vector during an accumulation attack scenario with skewed error distributions.