Abstract
Techniques are disclosed for privacy enforcement in streaming data aggregation. Contextual signals for an aggregation breakdown are obtained, including signals such as breakdown cardinality, traffic volume, targeting specificity, data age, regulatory tier, and cross-breakdown overlap. A risk score in a bounded range is computed and mapped to a dynamic k-anonymity threshold between configured minimum and maximum values, optionally adjusted for composition risk based on how many breakdowns a user contributes to. An existing k-anonymity check uses the dynamic threshold to gate release. Separately or in a closed loop, probabilistic cardinality sketches are monitored over time to forecast whether cohorts will meet k by a release time, producing a privacy health score and enabling graduated mitigations such as extending aggregation windows, coarsening breakdowns, pausing ingestion, or terminating doomed cohorts early.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Dynamic K-Anonymity", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10641