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ABSTRACT. This talk presents a formal approach to carrying out privacy pre- serving text perturbation using a variant of Differential Privacy (DP).
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This talk presents a formal approach to carrying out privacy preserving text perturbation using a variant of Differential Privacy (DP) known as Metric DP (mDP).
Calibrating Mechanisms for Privacy Preserving Text Analysis ; Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing - Colocated ...
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This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of dχ-privacy designed to achieve geo- ...
Calibrating Mechanisms for Privacy Preserving Text Analysis. O. Feyisetan, B. Balle, T. Diethe, and T. Drake. PrivateNLP@WSDM, volume 2573 of CEUR Workshop ...
Co-authors ; Calibrating Mechanisms for Privacy Preserving Text Analysis. O Feyisetan, B Balle, T Diethe, T Drake. PrivateNLP@ WSDM, 8-11, 2020. 1, 2020 ; Data- ...
Oct 20, 2019 · The second requirement is that the mechanism must scale to large amounts of data and be able to deal with datasets that grow over time. This ...
•Mechanism: modify the query to protect privacy whilst preserving semantics. •Our approach: Generalized Metric. Differential Privacy. Differential Privacy.
Differentially-private mechanisms for text gen- eration typically add carefully calibrated noise to input words and use the nearest neighbor.