Bet-or-Pass: Adversarially Robust Bloom Filters

Naor, Moni; Oved, Noa

doi:10.1007/978-3-031-22365-5_27

Cited by 1 publication

(1 citation statement)

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“…The widespread adoption of data sketches has put their security and privacy in the spotlight and recent works have shown that many of them are vulnerable in some settings [19], [20]. In particular, in the case of approximate membership check filters, the security and privacy of Bloom filters has been extensively studied showing that both their performance can be degraded and data can be extracted from the filter under certain conditions [21], [22], [23], [24], [25]. The security of cuckoo filters has also been studied showing that an attacker can create insertion failures [26].…”

Section: Introductionmentioning

confidence: 99%

On the Security of Quotient Filters: Attacks and Potential Countermeasures

Reviriego,

González,

Dayan

et al. 2024

IEEE Trans. Comput.

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The security of probabilistic data structures is increasingly important due to their wide adoption in many computing systems and applications. In particular, the security of approximate membership check filters such as Bloom or cuckoo filters has been recently studied showing how an attacker can degrade the filter performance in some settings. In this paper, we consider for the first time the security of another popular approximate membership check filter, the Quotient Filter (QF). Our analysis and simulations show that quotient filters are vulnerable to both white and black box attackers that can cause insertion failures and degrade the filter performance very significantly. An interesting finding is that quotient filters are vulnerable to a new type of attack, not applicable to Bloom or cuckoo filters, that can degrade the speed of queries dramatically. The paper also briefly discusses and evaluates potential countermeasures to detect and protect against those attacks.

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