391 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
Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices
In this paper a homomorphic privacy preserving association rule mining
algorithm is proposed which can be deployed in resource constrained devices
(RCD). Privacy preserved exchange of counts of itemsets among distributed
mining sites is a vital part in association rule mining process. Existing
cryptography based privacy preserving solutions consume lot of computation due
to complex mathematical equations involved. Therefore less computation involved
privacy solutions are extremely necessary to deploy mining applications in RCD.
In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets
encrypted by all mining sites without revealing individual values. The proposed
algorithm is built on with a well known communication efficient association
rule mining algorithm named count distribution (CD). Security proofs along with
performance analysis and comparison show the well acceptability and
effectiveness of the proposed algorithm. Efficient and straightforward privacy
model and satisfactory performance of the protocol promote itself among one of
the initiatives in deploying data mining application in RCD.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
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
Impacts of frequent itemset hiding algorithms on privacy preserving data mining
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2010Includes bibliographical references (leaves: 54-58)Text in English; Abstract: Turkish and Englishx, 69 leavesThe invincible growing of computer capabilities and collection of large amounts of data in recent years, make data mining a popular analysis tool. Association rules (frequent itemsets), classification and clustering are main methods used in data mining research. The first part of this thesis is implementation and comparison of two frequent itemset mining algorithms that work without candidate itemset generation: Matrix Apriori and FP-Growth. Comparison of these algorithms revealed that Matrix Apriori has higher performance with its faster data structure. One of the great challenges of data mining is finding hidden patterns without violating data owners. privacy. Privacy preserving data mining came into prominence as a solution. In the second study of the thesis, Matrix Apriori algorithm is modified and a frequent itemset hiding framework is developed. Four frequent itemset hiding algorithms are proposed such that: i) all versions work without pre-mining so privacy breech caused by the knowledge obtained by finding frequent itemsets is prevented in advance, ii) efficiency is increased since no pre-mining is required, iii) supports are found during hiding process and at the end sanitized dataset and frequent itemsets of this dataset are given as outputs so no post-mining is required, iv) the heuristics use pattern lengths rather than transaction lengths eliminating the possibility of distorting more valuable data
Literature Review on Secure Mining of Association Rules in Horizontally Distributed Databases
Data and knowledge Engineering is one of the area under data mining. Which can extract important knowledge from large database, but sometimes these database are divided among various parties. This paper addresses a fast distributed mining of association rules over horizontally distributed data. This paper presents different methods for secure mining of association rules in horizontally distributed databases. The main aim of this paper is protocol for secure mining of association rules in horizontally distributed databases. The current main protocol is that of Kantarcioglu and Clifton. This protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al., which is an unsecured distributed version of the Apriori algorithm. The main components in this protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. This protocol offers improved privacy with respect to the protocol in. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost
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
Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing
With the onset of the Information Era and the rapid growth of information
technology, ample space for processing and extracting data has opened up.
However, privacy concerns may stifle expansion throughout this area. The
challenge of reliable mining techniques when transactions disperse across
sources is addressed in this study. This work looks at the prospect of creating
a new set of three algorithms that can obtain maximum privacy, data utility,
and time savings while doing so. This paper proposes a unique double encryption
and Transaction Splitter approach to alter the database to optimize the data
utility and confidentiality tradeoff in the preparation phase. This paper
presents a customized apriori approach for the mining process, which does not
examine the entire database to estimate the support for each attribute.
Existing distributed data solutions have a high encryption complexity and an
insufficient specification of many participants' properties. Proposed solutions
provide increased privacy protection against a variety of attack models.
Furthermore, in terms of communication cycles and processing complexity, it is
much simpler and quicker. Proposed work tests on top of a realworld transaction
database demonstrate that the aim of the proposed method is realistic
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