# A Path to Holistic Privacy in Stream Processing Systems

arXiv: https://arxiv.org/abs/2305.11638 · HTML: https://mlsanity.com/paper/2305.11638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 5 of 5)

- [Task-aware Privacy Preservation for Multi-dimensional Data](https://mlsanity.com/paper/2110.02329/) (2021)
- [Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms](https://mlsanity.com/paper/1809.07750/) (2018)
- [Roadmap for Edge AI: A Dagstuhl Perspective](https://mlsanity.com/paper/2112.00616/) (2021)
- [FastZIP: Faster and More Secure Zero-Interaction Pairing](https://mlsanity.com/paper/2106.04907/) (2021)
- [No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems](https://mlsanity.com/paper/2305.00871/) (2023)

## Abstract (arXiv)

The massive streams of Internet of Things (IoT) data require a timely analysis to retain data usefulness. Stream processing systems (SPSs) enable this task, deriving knowledge from the IoT data in real-time. Such real-time analytics benefits many applications but can also be used to violate user privacy, as the IoT data collected from users or their vicinity is inherently sensitive. In this paper, we present our systematic look into privacy issues arising from the intersection of SPSs and IoT, identifying key research challenges towards achieving holistic privacy protection in SPSs and proposing the solutions.