Graph-Calibrated Differential Privacy for Correlated IoT Sensing Streams
Zhengxuan Chen, Junming Chen · 2026
Read Graph-Calibrated Differential Privacy for Correlated IoT Sensing Streams by Zhengxuan Chen, Junming Chen (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13568206.txt.
Description
Zhengxuan Chen and Junming Chen recalibrate differential privacy for sensor streams that leak through their correlations, in a 2026 paper. Internet of Things data braid time, place, and channels together, so a release that is private on paper can still betray an attribute through the braid. Standard privacy guards a declared unit, which may be narrower than the secret that matters.
Their graph-calibrated scheme ranks each component's exposure on a lag-aware graph, then spends the privacy budget accordingly, with certified sensitivity bounds per coordinate. Tests on four public data sets show better task utility and weaker attribute inference than baselines. Correlation, long privacy's blind spot, gets priced in.
Catalog record
| CREATOR | Zhengxuan Chen, Junming Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |