In-Storage Computation of Histograms with differential privacy

Abstract

Network-attached Smart Storage is becoming increasingly common in data analytics applications. It relies on processing elements, such as FPGAs, close to the storage medium to offload compute-intensive operations, reducing data movement across distributed nodes in the system. As a result, it can offer outstanding performance and energy efficiency. Modern data analytics systems are not only becoming more distributed they are also increasingly focusing on privacy policy compliance. This means that, in the future, Smart Storage will have to offload more and more privacy-related processing. In this work, we explore how the computation of differentially private (DP) histograms, a basic building block of privacy-preserving analytics, can be offloaded to FPGAs. By performing DP aggregation on the storage side, untrusted clients can be allowed to query the data in aggregate form without risking the leakage of personally identifiable information. We prototype our idea by extending an FPGA-based distributed key-value store with three new components. First, a histogram module, that processes values at 100Gbps line-rate. Second, a random noise generator that adds noise to final histogram according to the rules dictated by DP. Third, a mechanism to limit the rate at which key-value pairs can be used in histograms, to stay within the DP privacy budget.