Data Reconstruction: When You See It and When You Don't
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Abstract (arXiv)
We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the concept of reconstruction attacks by addressing two complementing questions: (i) What conditions guarantee that a given system is protected against such attacks? (ii) Under what circumstances does a given attack clearly indicate that a system is not protected? More specifically, * We introduce a new definitional paradigm -- Narcissus Resiliency -- to formulate a security definition for protection against reconstruction attacks. This paradigm has a self-referential nature that enables it to circumvent shortcomings of previously studied notions of security. Furthermore, as a side-effect, we demonstrate that Narcissus resiliency captures as special cases multiple well-studied concepts including differential privacy and other security notions of one-way functions and encryption schemes. * We formulate a link between reconstruction attacks and Kolmogorov complexity. This allows us to put forward a criterion for evaluating when such attacks are convincingly successful.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 May 2024.
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Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacycited by 15
- 2025A Unified Framework for Adversary-Aware Differential Privacy Boundscited by 2
- 2025Reconstruction and Secrecy under Approximate Distance Queriescited by 1
- 2026Beyond Indistinguishability: Measuring Extraction Risk in LLM APIscited by 1
- 2025Training Set Reconstruction from Differentially Private Forests: How Effective is DP?cited by 0
- 2025Bayesian Perspective on Memorization and Reconstructioncited by 0
- 2026SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)cited by 0
- 2026SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)6 Jun 2026
- 2026Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs20 Apr 2026
- 2025Reconstruction and Secrecy under Approximate Distance Queries9 Nov 2025
- 2025A Unified Framework for Adversary-Aware Differential Privacy Bounds10 Jul 2025
- 2025Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy9 Jul 2025
- 2025Bayesian Perspective on Memorization and Reconstruction29 May 2025
- 2025Training Set Reconstruction from Differentially Private Forests: How Effective is DP?7 Feb 2025