@inproceedings{raptors2026-c090,
  title     = {{Real-Time Privacy-Preserving AI Infrastructure Using Distributed k-Anonymity Enforcement}},
  author    = {Bethamsetty, Vamsi Viswanath},
  booktitle = {Proceedings of the Raptors Conference},
  volume    = {1},
  year      = {2026},
  publisher = {Hackathon Raptors},
  address   = {London, United Kingdom},
  doi       = {10.68495/rc.2026.C090},
  url       = {https://doi.org/10.68495/rc.2026.C090},
  abstract  = {Modern AI platforms processing billions of streaming events face a hard production problem: enforcing user privacy before data reaches machine learning systems, without breaking latency or reliability guarantees. This paper presents a distributed k-anonymity architecture built for real-time streaming pipelines, covering adaptive generalization, policy-aware buffering, validation gates and fault-tolerant aggregation. It is drawn from the engineering tradeoffs that arise once such a system is actually running: sparse cohorts that cannot be anonymized without losing the signal, late-arriving events that disturb cohorts already formed, operational bypass risks created by the need to debug, and maintaining policy consistency across live and replayed datasets, which is where most implementations quietly diverge. The argument is that privacy enforcement belongs in the infrastructure as a platform-level capability rather than as an isolated preprocessing step. Readers take away patterns for fail-safe streaming, cohort construction, and compliant regeneration of historical data.}
}