Masked LARk: Masked Learning, Aggregation and Reporting worKflow

• URL: http://arxiv.org/abs/2110.14794v1
• Date: Wed, 27 Oct 2021 21:59:37 GMT
• Title: Masked LARk: Masked Learning, Aggregation and Reporting worKflow
• Authors: Joseph J. Pfeiffer III and Denis Charles and Davis Gilton and Young Hun Jung and Mehul Parsana and Erik Anderson
• Abstract summary: Many web advertising data flows involve passive cross-site tracking of users. Most browsers are moving towards removal of 3PC in subsequent browser iterations. We propose a new proposal, called Masked LARk, for aggregation of user engagement measurement and model training.
• Score: 6.484847460164177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today, many web advertising data flows involve passive cross-site tracking of users. Enabling such a mechanism through the usage of third party tracking cookies (3PC) exposes sensitive user data to a large number of parties, with little oversight on how that data can be used. Thus, most browsers are moving towards removal of 3PC in subsequent browser iterations. In order to substantially improve end-user privacy while allowing sites to continue to sustain their business through ad funding, new privacy-preserving primitives need to be introduced. In this paper, we discuss a new proposal, called Masked LARk, for aggregation of user engagement measurement and model training that prevents cross-site tracking, while remaining (a) flexible, for engineering development and maintenance, (b) secure, in the sense that cross-site tracking and tracing are blocked and (c) open for continued model development and training, allowing advertisers to serve relevant ads to interested users. We introduce a secure multi-party compute (MPC) protocol that utilizes "helper" parties to train models, so that once data leaves the browser, no downstream system can individually construct a complete picture of the user activity. For training, our key innovation is through the usage of masking, or the obfuscation of the true labels, while still allowing a gradient to be accurately computed in aggregate over a batch of data. Our protocol only utilizes light cryptography, at such a level that an interested yet inexperienced reader can understand the core algorithm. We develop helper endpoints that implement this system, and give example usage of training in PyTorch.

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