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Aegis: A Correlation-Based Data Masking Advisor for Data-Sharing Ecosystems

Summary: Aegis picks masking configurations by minimizing predictive-utility deviation via preserved feature–label correlations, working with limited or no raw data. An IPF-based joint estimator enables MI/chi-square/g3 metrics and fast search for privacy-compliant masks that retain downstream ML accuracy. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7543
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,339 | 29.07%
DOI
10.1145/3769757

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BibTeX Citation

@inproceedings{laskar_sigmod26,
        title = {{Aegis: A Correlation-Based Data Masking Advisor for Data-Sharing Ecosystems}},
        author = {Laskar, Omar Islam and Khozestani, Fatemeh Ramezani and Nankani, Ishika and Nia, Sohrab Namazi and Roy, Senjuti Basu and Beedkar, Kaustubh},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769757},
        url = {https://dl.acm.org/doi/10.1145/3769757},
        year = {2026}
}

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