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

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy

Privacy-preserving distributed data mining is the study of mining on distributed data—owned by multiple data owners—in a non-secure environment, where the mining protocol does not reveal any sensitive information to the data owners, the individual privacy is preserved, and the output mining model is practically useful. In this thesis, we propose a secure two-party protocol for building a privacy-preserving decision tree classifier over distributed data using differential privacy. We utilize secure multiparty computation to ensure that the protocol is privacy-preserving. Our algorithm also utilizes parallel and sequential compositions, and applies distributed exponential mechanism to ensure that the output is differentially-private. We implemented our protocol in a distributed environment on real-life data, and the experimental results show that the protocol produces decision tree classifiers with high utility while being reasonably efficient and scalable

Privacy-Preserving Clustering of Data Streams

[[abstract]]As most previous studies on privacy-preserving data mining placed specific importance on the security of massive amounts of data from a static database, consequently data undergoing privacy-preservation often leads to a decline in the accuracy of mining results. Furthermore, following by the rapid advancement of Internet and telecommunication technology, subsequently data types have transformed from traditional static data into data streams with consecutive, rapid, temporal, and unpredictable properties. Due to the increase of such data types, traditional privacy-preserving data mining algorithms requiring complex calculation are no longer applicable. As a result, this paper has proposed a method of Privacy-Preserving Clustering of Data Streams (PPCDS) to improve data stream mining procedures while concurrently preserving privacy with a high degree of mining accuracy. PPCDS is mainly composed of two phases: Rotation-Based Perturbation and cluster mining. In the phase of data rotating perturbation phase, a rotation transformation matrix is applied to rapidly perturb the data streams in order to preserve data privacy. In the cluster mining phase, perturbed data will first establish a micro-cluster through optimization of cluster centers, then applying statistical calculation to update a micro-cluster, as well as using geometric time frame to allocate and store a micro-cluster, and finally output mining result through a macro-cluster generation. Two simple data structure are added in the macro-cluster generation process to avoid recalculating the distance between the macro-point and the cluster center in the generation process. This process reduces the repeated calculation time in order to enhance mining efficiency without losing mining accuracy.[[incitationindex]]EI[[booktype]]紙

Fusion: Privacy-preserving distributed protocol for high-dimensional data mashup

© 2015 IEEE. In the last decade, several approaches concerning private data release for data mining have been proposed. Data mashup, on the other hand, has recently emerged as a mechanism for integrating data from several data providers. Fusing both techniques to generate mashup data in a distributed environment while providing privacy and utility guarantees on the output involves several challenges. That is, how to ensure that no unnecessary information is leaked to the other parties during the mashup process, how to ensure the mashup data is protected against certain privacy threats, and how to handle the high-dimensional nature of the mashup data while guaranteeing high data utility. In this paper, we present Fusion, a privacy-preserving multi-party protocol for data mashup with guaranteed LKC-privacy for the purpose of data mining. Experiments on real-life data demonstrate that the anonymous mashup data provide better data utility, the approach can handle high dimensional data, and it is scalable with respect to the data size

Privacy Preserving Data Mining

Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels

Secure MultiParty Protocol for Differentially-Private Data Release

In the era where big data is the new norm, a higher emphasis has been placed on models which guarantees the release and exchange of data. The need for privacy-preserving data arose as more sophisticated data-mining techniques led to breaches of sensitive information. In this thesis, we present a secure multiparty protocol for the purpose of integrating multiple datasets simultaneously such that the contents of each dataset is not revealed to any of the data owners, and the contents of the integrated data do not compromise individual’s privacy. We utilize privacy by simulation to prove that the protocol is privacy-preserving, and we show that the output data satisfies ϵ-differential privacy

Privacy-Preserving Trajectory Data Publishing via Differential Privacy

Over the past decade, the collection of data by individuals, businesses and government agencies has increased tremendously. Due to the widespread of mobile computing and the advances in location-acquisition techniques, an immense amount of data concerning the mobility of moving objects have been generated. The movement data of an object (e.g. individual) might include specific information about the locations it visited, the time those locations were visited, or both. While it is beneficial to share data for the purpose of mining and analysis, data sharing might risk the privacy of the individuals involved in the data. Privacy-Preserving Data Publishing (PPDP) provides techniques that utilize several privacy models for the purpose of publishing useful information while preserving data privacy. The objective of this thesis is to answer the following question: How can a data owner publish trajectory data while simultaneously safeguarding the privacy of the data and maintaining its usefulness? We propose an algorithm for anonymizing and publishing trajectory data that ensures the output is differentially private while maintaining high utility and scalability. Our solution comprises a twofold approach. First, we generalize trajectories by generalizing and then partitioning the timestamps at each location in a differentially private manner. Next, we add noise to the real count of the generalized trajectories according to the given privacy budget to enforce differential privacy. As a result, our approach achieves an overall epsilon-differential privacy on the output trajectory data. We perform experimental evaluation on real-life data, and demonstrate that our proposed approach can effectively answer count and range queries, as well as mining frequent sequential patterns. We also show that our algorithm is efficient w.r.t. privacy budget and number of partitions, and also scalable with increasing data size