A Two-Stage Architecture for Differentially Private Kalman Filtering and\n LQG Control
Bibliographic record
Abstract
Large-scale monitoring and control systems enabling a more intelligent\ninfrastructure increasingly rely on sensitive data obtained from private\nagents, e.g., location traces collected from the users of an intelligent\ntransportation system. In order to encourage the participation of these agents,\nit becomes then critical to design algorithms that process information in a\nprivacy-preserving way. This article revisits the Kalman filtering and Linear\nQuadratic Gaussian (LQG) control problems, subject to privacy constraints. We\naim to enforce differential privacy, a formal, state-of-the-art definition of\nprivacy ensuring that the output of an algorithm is not too sensitive to the\ndata collected from any single participating agent. A two-stage architecture is\nproposed that first aggregates and combines the individual agent signals before\nadding privacy-preserving noise and post-filtering the result to be published.\nWe show a significant performance improvement offered by this architecture over\ninput perturbation schemes as the number of input signals increases and that an\noptimal static aggregation stage can be computed by solving a semidefinite\nprogram. The two-stage architecture, which we develop first for Kalman\nfiltering, is then adapted to the LQG control problem by leveraging the\nseparation principle. Numerical simulations illustrate the performance\nimprovements over differentially private algorithms without first-stage signal\naggregation.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".