Exploiting Time Channel Vulnerability of Learned Bloom Filters

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.