=Paper=
{{Paper
|id=Vol-2573/abstract2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2573/PrivateNLP_Poster3.pdf
|volume=Vol-2573
|dblpUrl=https://dblp.org/rec/conf/wsdm/FeyisetanB20
}}
==None==
https://ceur-ws.org/Vol-2573/PrivateNLP_Poster3.pdf
Privacy-Preserving Textual Analysis via Calibrated
Perturbations
Oluwaseyi Feyisetan Borja Balle
Amazon Amazon
sey@amazon.com pigem@amazon.co.uk
Thomas Drake Tom Diethe
Amazon Amazon
draket@amazon.com tdiethe@amazon.co.uk
ABSTRACT
Accurately learning from user data while providing quanti�able 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 -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 de�ned
by word embedding models. We present a privacy proof that satis-
�es d -privacy where the privacy parameter provides guarantees
with respect to a distance metric de�ned by the word embedding
space. We demonstrate how can be selected by analyzing plausible
deniability statistics backed up by large scale analysis on G��V�
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 on di�erent task types. Our results demonstrate prac-
tical utility (< 2% utility loss for training binary classi�ers) 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
Privacy- and Utility-Preserving Textual Analysis via Calibrated
Multivariate Perturbations
Oluwaseyi Feyisetan Borja Balle Thomas Drake Tom Diethe
sey@amazon.com borja.balle@gmail.com draket@amazon.com tdiethe@amazon.com
Summary A viable solution: Differential Privacy Mechanism Overview Sample results
•User’s goal: meet some specific need with ε-Differential Privacy (DP) bounds the
respect to an issued query x influence of any single input on the
•Agent’s goal: satisfy the user’s request output of a computation.
•Question: what occurs when x is used to Query Result 1
make other inferences about the user DP
Analysis
Attacker Bob’s data
•Mechanism: modify the query to protect
privacy whilst preserving semantics DP
Analysis
•Our approach: Generalized Metric Query Result 2
Differential Privacy.
Result 1 is approximately equal to Result 2
Introduction Differential Privacy Mechanism Details Experiment Results
What makes privacy difficult? Metric 6 12 17 23 29 35 41 47
Precision 0.00 0.00 0.00 0.00 0.67 0.90 0.93 1.00
High dimensional data Recall 0.00 0.00 0.00 0.00 0.02 0.09 0.14 0.30
Big and richer datasets lead Accuracy 0.50 0.50 0.50 0.50 0.51 0.55 0.57 0.65
to users generating AUC 0.06 0.04 0.11 0.36 0.61 0.85 0.88 0.93
uniquely identifiable
information. Scores measure privacy loss (lower is better)
Side knowledge
Innocuous data reveals
customer information
when joined with side-
knowledge.
Privacy in textual data Generalized Metric Differential Privacy Sampling and Calibration
NEW YORK TIMES ARTICLE
User Text
441779 dog that urinates on everything
441779 safest place to live
...
441779 the best season to visit Italy
441779 landscapers in Lilburn, GA
Most of the queries do not contain PII Utility of downstream machine learning model on data
(higher is better)
POSTER TEMPLATE BY:
www.PosterPresentations.com