# Building K-Anonymous User Cohorts with Consecutive Consistent Weighted Sampling (CCWS)

arXiv: https://arxiv.org/abs/2304.13677 · HTML: https://mlsanity.com/paper/2304.13677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [Differentially Private One Permutation Hashing and Bin-wise Consistent Weighted Sampling](https://mlsanity.com/paper/2306.07674/) (2023)
- [Pb-Hash: Partitioned b-bit Hashing](https://mlsanity.com/paper/2306.15944/) (2023)

## Abstract (arXiv)

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 >70M 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.