Welcome to the resource topic for 2024/1911
Title:
Deletions and Dishonesty: Probabilistic Data Structures in Adversarial Settings
Authors: Mia Filić, Keran Kocher, Ella Kummer, Anupama Unnikrishnan
Abstract:Probabilistic data structures (PDS) are compact representations of high-volume data that provide approximate answers to queries about the data. They are commonplace in today’s computing systems, finding use in databases, networking and more. While PDS are designed to perform well under benign inputs, they are frequently used in applications where inputs may be adversarially chosen. This may lead to a violation of their expected behaviour, for example an increase in false positive rate.
In this work, we focus on PDS that handle approximate membership queries (AMQ). We consider adversarial users with the capability of making adaptive insertions, deletions and membership queries to AMQ-PDS, and analyse the performance of AMQ-PDS under such adversarial inputs.
We argue that deletions significantly empower adversaries, presenting a challenge to enforcing honest behaviour when compared to insertion-only AMQ-PDS. To address this, we introduce a new concept of an honest setting for AMQ-PDS with deletions. By leveraging simulation-based security definitions, we then quantify how much harm can be caused by adversarial users to the functionality of AMQ-PDS. Our resulting bounds only require calculating the maximal false positive probability and insertion failure probability achievable in our novel honest setting.
We apply our results to Cuckoo filters and Counting filters. We show how to protect these AMQ-PDS at low cost, by replacing or composing the hash functions with keyed pseudorandom functions in their construction. This strategy involves establishing practical bounds for the probabilities mentioned above. Using our new techniques, we demonstrate that achieving security against adversarial users making both insertions and deletions remains practical.
ePrint: https://eprint.iacr.org/2024/1911
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