Building K-anonymous User Cohorts With Consecutive Consistent Weighted Sampling (CCWS)
Zheng Xinyi, Zhao Weijie, Li Xiaoyun, Li Ping. Arxiv 2023
To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such identity regime, an accurate and efficient cohort building algorithm is desired to group users with similar characteristics. In this paper, we propose a scalable \(K\)-anonymous cohort building algorithm called {\em consecutive consistent weighted sampling} (CCWS). The proposed method combines the spirit of the (\(p\)-powered) consistent weighted sampling and hierarchical clustering, so that the \(K\)-anonymity is ensured by enforcing a lower bound on the size of cohorts. Evaluations on a LinkedIn dataset consisting of \(>70\)M users and ads campaigns demonstrate that CCWS achieves substantial improvements over several hashing-based methods including sign random projections (SignRP), minwise hashing (MinHash), as well as the vanilla CWS.
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