43 citations · 76 across the 20 of their papers we have counts for
9 papers · 1 filter
Big Bird: Resilient Privacy Budgeting Across Untrusted Web Domains
Pierre Tholoniat, Alison Caulfield, Giorgio Cavicchioli +6
The W3C Attribution API is an emerging standard for privacy-preserving advertising measurement. Its current privacy architecture enforces individual differential privacy (IDP) inde…
Characterizing the Networks Sending Enterprise Phishing Emails
Elisa Luo, Liane Young, Grant Ho +4
Phishing attacks on enterprise employees present one of the most costly and potent threats to organizations. We explore an understudied facet of enterprise phishing attacks: the em…
Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
Pierre Tholoniat, Kelly Kostopoulou, Peter McNeely +6
With the impending removal of third-party cookies from major browsers and the introduction of new privacy-preserving advertising APIs, the research community has a timely opportuni…
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury +4
Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can…
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
Wei Hao, Aahil Awatramani, Jiayang Hu +5
Full-precision deep learning models are typically too large or costly to deploy on edge devices. To accommodate to the limited hardware resources, models are adapted to the edge us…
Privacy Budget Scheduling
Tao Luo, Mingen Pan, Pierre Tholoniat +3
Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on t…