Security, Privacy and Abuse Prevention

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces. We prove nearly tight bounds on the sample complexity of robust private learning of halfspaces for a large regime of parameters. A highlight of our results is that robust and private learning is harder than robust or private learning alone....

In this paper we prove that the sample complexity of properly learning a class of Littlestone dimension d with approximate differential privacy is Õ(d^6), ignoring privacy and accuracy parameters. This result answers a question of Bun et al. (FOCS 2020) by improving upon their upper bound of 2^O(d) on the sample complexity. Prior to our work, finiteness of the sample complexity for privately...

This document describes a secure mechanism to join sets of heterogeneous ids from multiple data providers (e.g. broadcasters, publishers, data enrichment providers) and create a set of encrypted common identifiers, which we refer to as SUMIDs. These identifiers can be used for computing multi-party reach, frequency, sales lift, MTA, and other ads metrics. We also introduce the concept of “match...

Secure applications implement protections against side-channel and physical attacks. Such protections embed input/output side-effects preventing optimizing compilers from altering the protection. These side-effects are error-prone and compiler-dependent, and the current practice involves analyzing the generated machine code to make sure security or privacy properties are still enforced. Vu et...