{
  "abstract": "State estimation is widely used in networked dynamical systems, but released estimates may inadvertently reveal sensitive information about private state variables. Existing privacy-preserving estimation frameworks often do not explicitly account for adversarial prior knowledge. Motivated by this gap, we study privacy-preserving state estimation for discrete-time linear stochastic systems under prior information exposure using pointwise maximal leakage (PML). We first reveal a privacy anti-optimality property of the Kalman filter: in the absence of additive release noise, the steady-state Kalman filter maximizes PML privacy leakage among the considered class of stable linear estimators. We further establish a nontrivial lower bound on the steady-state estimation error enforced by $(\\varepsilon,δ)$-PML privacy. We then derive sufficient release-noise conditions that guarantee $(\\varepsilon,δ)$-PML privacy, first for the Kalman filter and then for stable linear estimators. Finally, a convex semidefinite program is formulated for a joint design of the estimator gain and release noise covariance, providing a tractable privacy-preserving estimator with an explicit upper bound on the steady-state estimation error. Simulations on a source--load power system demonstrate the proposed privacy--utility tradeoff.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16556",
  "author_total": 3,
  "authors": [
    {
      "name": "Le Liu"
    },
    {
      "name": "Yu Kawano"
    },
    {
      "name": "Ming Cao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1rmvygkeehxkq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16556",
  "fulltext_url": null,
  "id": "1rmvygkeehxkq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16556",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7221809999",
  "pdf_url": "https://arxiv.org/pdf/2610.16556",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1rmvygkeehxkq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.16556",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16556"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Design of Privacy-Preserving Estimators Under Prior Information Exposure",
  "title_text": "Design of Privacy-Preserving Estimators Under Prior Information Exposure",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/1rmvygkeehxkq",
  "venue": "arXiv",
  "versions": []
}