What question did this study set out to answer?

To develop and evaluate an analytically optimal attack methodology for inferring confidential records stored in databases protected by mixed property-revealing and semantically secure encryption.

July 1, 2018Proceedings of the VLDB Endowment

The tao of inference in privacy-protected databases

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To develop and evaluate an analytically optimal attack methodology for inferring confidential records stored in databases protected by mixed property-revealing and semantically secure encryption.
Formulated an analytically optimal multinomial attack targeting property-revealing encryption (PRE), extending it to exploit cross-column correlations.
Coupled PRE inference outputs with machine learning and record linkage to infer attributes shielded by semantically secure encryption or redaction.
Evaluated the methodology on empirical medical, census, and union-membership datasets.
Outperformed the best prior heuristic attack on PRE-encrypted attributes, such as demographics and ZIP codes, by a factor of 16.
Successfully reconstructed strongly encrypted attributes, including incomes and medical diagnoses, achieving 97% accuracy when predicting mental health or substance abuse conditions in hospital discharge data.

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Implication

Computational study demonstrates full-record data recovery in encrypted databases using multinomial attacks, highlighting security risks in mixed-encryption designs.

Cite This Study

Bindschaedler et al. (2018) studied this question.

synapsesocial.com/papers/6a1abfaa9fa30811a0b91191 https://doi.org/10.14778/3236187.3236217

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Implication

Computational study demonstrates full-record data recovery in encrypted databases using multinomial attacks, highlighting security risks in mixed-encryption designs.

The tao of inference in privacy-protected databases

Key Points

Discussion

Implication

Cite This Study

The tao of inference in privacy-protected databases

Key Points

Discussion

Implication

Cite This Study

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