41,945 research outputs found
Privacy Preserving Utility Mining: A Survey
In big data era, the collected data usually contains rich information and
hidden knowledge. Utility-oriented pattern mining and analytics have shown a
powerful ability to explore these ubiquitous data, which may be collected from
various fields and applications, such as market basket analysis, retail,
click-stream analysis, medical analysis, and bioinformatics. However, analysis
of these data with sensitive private information raises privacy concerns. To
achieve better trade-off between utility maximizing and privacy preserving,
Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent
years. In this paper, we provide a comprehensive overview of PPUM. We first
present the background of utility mining, privacy-preserving data mining and
PPUM, then introduce the related preliminaries and problem formulation of PPUM,
as well as some key evaluation criteria for PPUM. In particular, we present and
discuss the current state-of-the-art PPUM algorithms, as well as their
advantages and deficiencies in detail. Finally, we highlight and discuss some
technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page
Mining Frequent Graph Patterns with Differential Privacy
Discovering frequent graph patterns in a graph database offers valuable
information in a variety of applications. However, if the graph dataset
contains sensitive data of individuals such as mobile phone-call graphs and
web-click graphs, releasing discovered frequent patterns may present a threat
to the privacy of individuals. {\em Differential privacy} has recently emerged
as the {\em de facto} standard for private data analysis due to its provable
privacy guarantee. In this paper we propose the first differentially private
algorithm for mining frequent graph patterns.
We first show that previous techniques on differentially private discovery of
frequent {\em itemsets} cannot apply in mining frequent graph patterns due to
the inherent complexity of handling structural information in graphs. We then
address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling
based algorithm. Unlike previous work on frequent itemset mining, our
techniques do not rely on the output of a non-private mining algorithm.
Instead, we observe that both frequent graph pattern mining and the guarantee
of differential privacy can be unified into an MCMC sampling framework. In
addition, we establish the privacy and utility guarantee of our algorithm and
propose an efficient neighboring pattern counting technique as well.
Experimental results show that the proposed algorithm is able to output
frequent patterns with good precision
Reverse-Safe Data Structures for Text Indexing
We introduce the notion of reverse-safe data structures. These are data structures that prevent the reconstruction of the data they encode (i.e., they cannot be easily reversed). A data structure D is called z-reverse-safe when there exist at least z datasets with the same set of answers as the ones stored by D. The main challenge is to ensure that D stores as many answers to useful queries as possible, is constructed efficiently, and has size close to the size of the original dataset it encodes. Given a text of length n and an integer z, we propose an algorithm which constructs a z-reverse-safe data structure that has size O(n) and answers pattern matching queries of length at most d optimally, where d is maximal for any such z-reverse-safe data structure. The construction algorithm takes O(n ω log d) time, where ω is the matrix multiplication exponent. We show that, despite the n ω factor, our engineered implementation takes only a few minutes to finish for million-letter texts. We further show that plugging our method in data analysis applications gives insignificant or no data utility loss. Finally, we show how our technique can be extended to support applications under a realistic adversary model
Enabling Multi-level Trust in Privacy Preserving Data Mining
Privacy Preserving Data Mining (PPDM) addresses the problem of developing
accurate models about aggregated data without access to precise information in
individual data record. A widely studied \emph{perturbation-based PPDM}
approach introduces random perturbation to individual values to preserve
privacy before data is published. Previous solutions of this approach are
limited in their tacit assumption of single-level trust on data miners.
In this work, we relax this assumption and expand the scope of
perturbation-based PPDM to Multi-Level Trust (MLT-PPDM). In our setting, the
more trusted a data miner is, the less perturbed copy of the data it can
access. Under this setting, a malicious data miner may have access to
differently perturbed copies of the same data through various means, and may
combine these diverse copies to jointly infer additional information about the
original data that the data owner does not intend to release. Preventing such
\emph{diversity attacks} is the key challenge of providing MLT-PPDM services.
We address this challenge by properly correlating perturbation across copies at
different trust levels. We prove that our solution is robust against diversity
attacks with respect to our privacy goal. That is, for data miners who have
access to an arbitrary collection of the perturbed copies, our solution prevent
them from jointly reconstructing the original data more accurately than the
best effort using any individual copy in the collection. Our solution allows a
data owner to generate perturbed copies of its data for arbitrary trust levels
on-demand. This feature offers data owners maximum flexibility.Comment: 20 pages, 5 figures. Accepted for publication in IEEE Transactions on
Knowledge and Data Engineerin
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