Exploiting Time Channel Vulnerability of Learned Bloom Filters

Harman Singh Farwah, Gagandeep Singh, Cheng Tan

Keywords: bloom-filter, learned-bloom-filter, neural-network-for-systems

TL;DR: This paper examines the security of learned bloom filters and introduces an attack to recover system owners’ data via a timing side channel vulnerability.

Abstract: Neural network for computer systems—such as operating systems, databases, and network systems—attract much attention. However, using neural networks in systems introduces new attacking surfaces. This paper makes the first attempt to study the security factor of learned bloom filters, a promising neural network based data structure in systems. We design and implement an attack that can efficiently recover system owners’ data via a timing side channel and a new recovering algorithm.

Submission Number: 160