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Don’t Be a Tattle-Tale: Preventing Leakages through Data Dependencies on Access Control Protected Data

Summary: Proposes a full deniability security model to prevent any inference about sensitive data from query answers via data dependencies. Offers an algorithm that minimally hides non-sensitive cells to achieve deniability, with experiments showing practicality. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12923
Venue
VLDB
Year
2022
Pagerank
5.2103651e-05
Overall Rank
9,838 | 32.51%
DOI
10.14778/3551793.3551805

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{pappachan_vldb22,
        title = {{Don’t Be a Tattle-Tale: Preventing Leakages through Data Dependencies on Access Control Protected Data}},
        author = {Pappachan, Primal and Zhang, Shufan and He, Xi and Mehrotra, Sharad},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2437--2449},
        doi = {10.14778/3551793.3551805},
        url = {https://doi.org/10.14778/3551793.3551805},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,376 DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance 2023 SIGMOD 5.893174e-05
7,503 Disclosure-Compliant Query Answering 2024 SIGMOD 5.6029996e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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