Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning

Zain ul Abi Din Panagiotis Tigas Samuel T. King Benjamin Livshits

2020

Publication year

Usenix ATC

Venue

Peer-reviewed

Type

§ Abstract

Problem

Contextual bandit algorithms (CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users.

Approach

Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users. This paper proposes a technique we call Privacy-Preserving Bandits (P2B); a system that updates local agents by collecting feedback from other agents in a differentially-private manner.

Results

Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed only a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget ≈ 0.693. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization.

Cite this paper — BibTeX

@InProceedings{mlsys20,
  title = "{Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning}",
  author = "Zain ul Abi Din and Panagiotis Tigas and Samuel T. King and Benjamin Livshits",
  year = "2020",
  month = jul,
  booktitle = "Usenix ATC",
}