50 research outputs found
Researching Big Data Research: Ethical Implications for IS Scholars
This ERF (Emerging Research Forum) paper focuses on the ethical implications of IS academic big data research. We explore how big data research raises concerns about privacy, human subjects protection and research integrity that are not yet adequately addressed by law, regulation, or the norms of acceptable research conduct. The objective is to increase awareness and promote constructive debate, with the ultimate goal of developing consensus in the field about appropriate research data use practices
Tight Lower Bounds for Differentially Private Selection
A pervasive task in the differential privacy literature is to select the
items of "highest quality" out of a set of items, where the quality of each
item depends on a sensitive dataset that must be protected. Variants of this
task arise naturally in fundamental problems like feature selection and
hypothesis testing, and also as subroutines for many sophisticated
differentially private algorithms.
The standard approaches to these tasks---repeated use of the exponential
mechanism or the sparse vector technique---approximately solve this problem
given a dataset of samples. We provide a tight lower
bound for some very simple variants of the private selection problem. Our lower
bound shows that a sample of size is required
even to achieve a very minimal accuracy guarantee.
Our results are based on an extension of the fingerprinting method to sparse
selection problems. Previously, the fingerprinting method has been used to
provide tight lower bounds for answering an entire set of queries, but
often only some much smaller set of queries are relevant. Our extension
allows us to prove lower bounds that depend on both the number of relevant
queries and the total number of queries
Differential Privacy for Sequential Algorithms
We study the differential privacy of sequential statistical inference and
learning algorithms that are characterized by random termination time. Using
the two examples: sequential probability ratio test and sequential empirical
risk minimization, we show that the number of steps such algorithms execute
before termination can jeopardize the differential privacy of the input data in
a similar fashion as their outputs, and it is impossible to use the usual
Laplace mechanism to achieve standard differentially private in these examples.
To remedy this, we propose a notion of weak differential privacy and
demonstrate its equivalence to the standard case for large i.i.d. samples. We
show that using the Laplace mechanism, weak differential privacy can be
achieved for both the sequential probability ratio test and the sequential
empirical risk minimization with proper performance guarantees. Finally, we
provide preliminary experimental results on the Breast Cancer Wisconsin
(Diagnostic) and Landsat Satellite Data Sets from the UCI repository