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)
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Authors
- 1. Omar Islam Laskar (Indian Institute of Technology Delhi)
- 2. Fatemeh Ramezani Khozestani (New Jersey Institute of Technology)
- 3. Ishika Nankani (Indian Institute of Technology Delhi)
- 4. Sohrab Namazi Nia (New Jersey Institute of Technology)
- 5. Senjuti Basu Roy (New Jersey Institute of Technology)
- 6. Kaustubh Beedkar (Indian Institute of Technology Delhi)
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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