Real-Time Privacy-Preserving AI Infrastructure Using Distributed k-Anonymity Enforcement
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.
- Published in
- Proceedings of the Raptors Conference, Vol. 1 (2026)
- Conference
- Raptors Conference 2026, 21 October 2026, online
- Accepted
- 2026-09-17
- DOI
- https://doi.org/10.68495/rc.2026.C090
- Presentation
- Slides are not yet available for this paper.
- Licence
- © 2026Vamsi Viswanath Bethamsetty. Published under Creative Commons Attribution 4.0 International (CC BY 4.0).
- Publisher
- Hackathon Raptors, Community Interest Company 15557917, London, United Kingdom
- Permanent address
- https://proceedings.raptors.dev/bethamsetty-real-time-privacy-preserving
Open access. Every entry in the Proceedings is free to read, with no charge to authors and no charge to readers. Slides are served from this site, so they stay available at the paper's permanent address.
Cite this paper
Vamsi Viswanath Bethamsetty (2026). Real-Time Privacy-Preserving AI Infrastructure Using Distributed k-Anonymity Enforcement. Proceedings of the Raptors Conference, Vol. 1 (2026). Hackathon Raptors, London. https://doi.org/10.68495/rc.2026.C090
@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.}
}
TY - CONF TI - Real-Time Privacy-Preserving AI Infrastructure Using Distributed k-Anonymity Enforcement AU - Bethamsetty, Vamsi Viswanath T2 - Proceedings of the Raptors Conference VL - 1 PY - 2026 DA - 2026/09/17 PB - Hackathon Raptors CY - London, United Kingdom DO - 10.68495/rc.2026.C090 UR - https://doi.org/10.68495/rc.2026.C090 AB - 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. LA - en ER -