Security, Privacy and Abuse Prevention

Differential privacy (DP) is a formal notion for quantifying the privacy loss of algorithms. Algorithms in the central model of DP achieve high accuracy but make the strongest trust assumptions whereas those in the local DP model make the weakest trust assumptions but incur substantial accuracy loss. The shuffled DP model (Bittau et al., 2017; Erlingsson et al., 2019; Cheu et al.,2019) has...

Robust secret sharing is a strengthening of secret sharing which allows the secret to be recovered even if some of the shares being used in the reconstruction have been adversarially modified. In this work, we study the setting where out of all the n shares, the adversary is allowed to adaptively corrupt and modify t shares, where n = 2t+1. It is known that in this setting, to recover a secret...

Malware is one of the key threats to online security today, with applications ranging from phishing mailers to ransomware andtrojans. Due to the sheer size and variety of the malware threat, it is impractical to combat it as a whole. Instead, governments and companies have instituted teams dedicated to identifying, prioritizing, and removing specific malware families that directly affect their...

The shuffled (aka anonymous) model has recently generated significant interest as a candidate dis- tributed privacy framework with trust assumptions better than the central model but with achievable error rates smaller than the local model. In this paper, we study pure differentially private protocols in the shuffled model for summation, a very basic and widely used primitive. Specifically: •...