9,361 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
Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness
A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets
Trading Indistinguishability-based Privacy and Utility of Complex Data
The collection and processing of complex data, like structured data or infinite streams, facilitates novel applications. At the same time, it raises privacy requirements by the data owners. Consequently, data administrators use privacy-enhancing technologies (PETs) to sanitize the data, that are frequently based on indistinguishability-based privacy definitions. Upon engineering PETs, a well-known challenge is the privacy-utility trade-off. Although literature is aware of a couple of trade-offs, there are still combinations of involved entities, privacy definition, type of data and application, in which we miss valuable trade-offs.
In this thesis, for two important groups of applications processing complex data, we study (a) which indistinguishability-based privacy and utility requirements are relevant, (b) whether existing PETs solve the trade-off sufficiently, and (c) propose novel PETs extending the state-of-the-art substantially in terms of methodology, as well as achieved privacy or utility. Overall, we provide four contributions divided into two parts. In the first part, we study applications that analyze structured data with distance-based mining algorithms. We reveal that an essential utility requirement is the preservation of the pair-wise distances of the data items. Consequently, we propose distance-preserving encryption (DPE), together with a general procedure to engineer respective PETs by leveraging existing encryption schemes. As proof of concept, we apply it to SQL log mining, useful for database performance tuning. In the second part, we study applications that monitor query results over infinite streams. To this end, -event differential privacy is state-of-the-art. Here, PETs use mechanisms that typically add noise to query results. First, we study state-of-the-art mechanisms with respect to the utility they provide. Conducting the so far largest benchmark that fulfills requirements derived from limitations of prior experimental studies, we contribute new insights into the strengths and weaknesses of existing mechanisms. One of the most unexpected, yet explainable result, is a baseline supremacy. It states that one of the two baseline mechanisms delivers high or even the best utility. A natural follow-up question is whether baseline mechanisms already provide reasonable utility. So, second, we perform a case study from the area of electricity grid monitoring revealing two results. First, achieving reasonable utility is only possible under weak privacy requirements. Second, the utility measured with application-specific utility metrics decreases faster than the sanitization error, that is used as utility metric in most studies, suggests. As a third contribution, we propose a novel differential privacy-based privacy definition called Swellfish privacy. It allows tuning utility beyond incremental -event mechanism design by supporting time-dependent privacy requirements. Formally, as well as by experiments, we prove that it increases utility significantly. In total, our thesis contributes substantially to the research field, and reveals directions for future research
A Framework for High-Accuracy Privacy-Preserving Mining
To preserve client privacy in the data mining process, a variety of
techniques based on random perturbation of data records have been proposed
recently. In this paper, we present a generalized matrix-theoretic model of
random perturbation, which facilitates a systematic approach to the design of
perturbation mechanisms for privacy-preserving mining. Specifically, we
demonstrate that (a) the prior techniques differ only in their settings for the
model parameters, and (b) through appropriate choice of parameter settings, we
can derive new perturbation techniques that provide highly accurate mining
results even under strict privacy guarantees. We also propose a novel
perturbation mechanism wherein the model parameters are themselves
characterized as random variables, and demonstrate that this feature provides
significant improvements in privacy at a very marginal cost in accuracy.
While our model is valid for random-perturbation-based privacy-preserving
mining in general, we specifically evaluate its utility here with regard to
frequent-itemset mining on a variety of real datasets. The experimental results
indicate that our mechanisms incur substantially lower identity and support
errors as compared to the prior techniques
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
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