Authors:
Mahboobeh Dorafshanian
and
Mohamed Mejri
Affiliation:
Department of Computer Science and Software Engineering, Laval University, QC, Canada
Keyword(s):
Differential Privacy, Risk, Data Privacy, Security, Big Data, Privacy Budget, Risk of Data Disclosure.
Abstract:
Companies have key concerns about privacy issues when dealing with big data. Many studies show that privacy
preservation models such as Anonymization, k-Anonymity, l-Diversity, and t-Closeness failed in many cases.
Differential Privacy techniques can address these issues by adding a random value (noise) to the query result
or databases rather than releasing raw data. Measuring the value of this noise (ε) is a controversial topic that
is difficult for managers to understand. To the best of our knowledge, a small number of works calculate the
value of ε. To this end, this paper provides an upper bound for the privacy budget ε based on a given risk
threshold when the Laplace noise is used. The risk is defined as the probability of leaking private information
multiplied by the impact of this disclosure. Estimating the impact is a great challenge as well as measuring
the privacy budget. This paper shows how databases like UT CID ITAP could be very useful to estimate these
kinds o
f impacts.
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