Paper Info
Title
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs.
Abstract
This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.
Year
DOI
Venue
2017
10.1145/3133956.3134097
CCS
Keywords
DocType
Volume
privacy, use privacy, proxy, causal analysis
Conference
abs/1705.07807
ISBN
Citations 
PageRank 
978-1-4503-4946-8
6
0.45
References 
Authors
19
5
Name
Order
Citations
PageRank
Anupam Datta1161787.21
Matt Fredrikson297248.56
Gihyuk Ko3100.91
Piotr Mardziel41069.37
Shayak Sen5968.89