Privacy-Preserving Textual Analysis via Calibrated ...ceur-ws.org › Vol-2573 ›...
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Privacy-Preserving Textual Analysis via Calibrated Perturbations Oluwaseyi Feyisetan Amazon [email protected] Borja Balle Amazon [email protected] Thomas Drake Amazon [email protected] Tom Diethe Amazon [email protected] ABSTRACT Accurately learning from user data while providing quantiable pri- vacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presents a formal approach to carrying out privacy preserving text perturbation using the no- tion of d χ -privacy designed to achieve geo-indistinguishability in location data. Our approach applies carefully calibrated noise to vector representation of words in a high dimension space as dened by word embedding models. We present a privacy proof that satis- es d χ -privacy where the privacy parameter ε provides guarantees with respect to a distance metric dened by the word embedding space. We demonstrate how ε can be selected by analyzing plausible deniability statistics backed up by large scale analysis on GV and T embeddings. We conduct privacy audit experiments against 2 baseline models and utility experiments on 3 datasets to demonstrate the tradeo between privacy and utility for varying values of ε on dierent task types. Our results demonstrate prac- tical utility (< 2% utility loss for training binary classiers) while providing better privacy guarantees than baseline models. ACM Reference Format: Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020. Privacy-Preserving Textual Analysis via Calibrated Perturbations. In Pro- ceedings of Workshop on Privacy and Natural Language Processing (Pri- vateNLP ’20). Houston, TX, USA, 1 page. https://doi.org/10.1145/nnnnnnn. nnnnnnn Copyright ©2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Presented at the PrivateNLP 2020 Workshop on Privacy in Natural Language Processing Colocated with 13th ACM International WSDM Conference, 2020, in Houston, Texas, USA. PrivateNLP ’20, February 7, 2020, Houston, TX, USA © 2020
Transcript of Privacy-Preserving Textual Analysis via Calibrated ...ceur-ws.org › Vol-2573 ›...
Privacy-Preserving Textual Analysis via CalibratedPerturbations
Oluwaseyi FeyisetanAmazon
Borja BalleAmazon
Thomas DrakeAmazon
Tom DietheAmazon
ABSTRACTAccurately learning from user data while providing quanti�able pri-vacy guarantees provides an opportunity to build better ML modelswhile maintaining user trust. This paper presents a formal approachto carrying out privacy preserving text perturbation using the no-tion of d� -privacy designed to achieve geo-indistinguishability inlocation data. Our approach applies carefully calibrated noise tovector representation of words in a high dimension space as de�nedby word embedding models. We present a privacy proof that satis-�es d� -privacy where the privacy parameter � provides guaranteeswith respect to a distance metric de�ned by the word embeddingspace. We demonstrate how � can be selected by analyzing plausibledeniability statistics backed up by large scale analysis on G��V�and ����T��� embeddings. We conduct privacy audit experimentsagainst 2 baseline models and utility experiments on 3 datasets todemonstrate the tradeo� between privacy and utility for varyingvalues of � on di�erent task types. Our results demonstrate prac-tical utility (< 2% utility loss for training binary classi�ers) whileproviding better privacy guarantees than baseline models.ACM Reference Format:Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020.Privacy-Preserving Textual Analysis via Calibrated Perturbations. In Pro-ceedings of Workshop on Privacy and Natural Language Processing (Pri-vateNLP ’20). Houston, TX, USA, 1 page. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Copyright ©2020 for this paper by its authors. Use permitted under Creative CommonsLicense Attribution 4.0 International (CC BY 4.0). Presented at the PrivateNLP 2020Workshop on Privacy in Natural Language Processing Colocated with 13th ACMInternational WSDM Conference, 2020, in Houston, Texas, USA.PrivateNLP ’20, February 7, 2020, Houston, TX, USA© 2020
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Privacy- and Utility-Preserving Textual Analysis via CalibratedMultivariate Perturbations
Oluwaseyi Feyisetan Borja Balle Thomas Drake Tom Diethe
Introduction
Generalized Metric Differential Privacy
Attacker
Query Result 1
Query Result 2
DPAnalysis
DPAnalysis
Bob’s data
ε-Differential Privacy (DP) bounds the influence of any single input on the output of a computation.
A viable solution: Differential Privacy Sample results
Privacy in textual data
Experiment Results
Metric 6 12 17 23 29 35 41 47Precision 0.00 0.00 0.00 0.00 0.67 0.90 0.93 1.00Recall 0.00 0.00 0.00 0.00 0.02 0.09 0.14 0.30Accuracy 0.50 0.50 0.50 0.50 0.51 0.55 0.57 0.65AUC 0.06 0.04 0.11 0.36 0.61 0.85 0.88 0.93
Scores measure privacy loss (lower is better)
Utility of downstream machine learning model on data(higher is better)
What makes privacy difficult?
High dimensional data
Side knowledgeInnocuous data reveals customer informationwhen joined with side-knowledge.
Big and richer datasets lead to users generating uniquely identifiable information.
User Text441779 dog that urinates on everything441779 safest place to live. . .441779 the best season to visit Italy441779 landscapers in Lilburn, GA
Result 1 is approximately equal to Result 2
Summary
•User’s goal: meet some specific need with respect to an issued query x•Agent’s goal: satisfy the user’s request•Question: what occurs when x is used to make other inferences about the user
•Mechanism: modify the query to protect privacy whilst preserving semantics•Our approach: Generalized Metric Differential Privacy.
Differential Privacy Mechanism Details
Mechanism Overview
Sampling and Calibration
Most of the queries do not contain PII
[email protected] [email protected] [email protected]@gmail.com
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