New Directions in Privacy-Preserving Ad Attribution -
Advertisement attribution allows websites selling goods to learn statistics on which advertisement campaigns can be attributed to converting sales. It is a core component of the advertising ecosystem and has typically been performed using third-party cookies. However, due to privacy and tracking concerns, many browsers have made the move to block third-party cookies. I will overview the approach taken in a new in-progress standard for privacy-preserving attribution in which aggregate statistics on attribution may be learned without revealing sensitive information about individuals; the standard is being developed in tandem with industry partners Google, Meta, Apple, Mozilla, and others. I will end by discussing concerns around ad fraud that becomes difficult to detect in the new privacy-preserving system, and overview some recent work at UW in developing cryptographic fraud mitigation add-ons compatible with the standard.

Bio:
Nirvan Tyagi joined the University of Washington last year as an Assistant Professor. He works as part of the UW Security & Privacy Lab and part of the UW Cryptography Group. His research interests focus on exploring secure, privacy-preserving systems, including anonymous communication, secure cloud storage, and machine learning privacy. Recently, he has focused on building systems that use cryptography to provide users with privacy and security while also enabling appropriate accountability against misbehavior. He is the recipient of an Early Career Award at CRYPTO 2020, and his work on one-time-use credentials has been standardized by the IETF.