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

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming

RANDOMIZATION BASED PRIVACY PRESERVING CATEGORICAL DATA ANALYSIS

The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today’s society. Since data mining often involves sensitive infor- mation of individuals, the public has expressed a deep concern about their privacy. Privacy-preserving data mining is a study of eliminating privacy threats while, at the same time, preserving useful information in the released data for data mining. This dissertation investigates data utility and privacy of randomization-based mod- els in privacy preserving data mining for categorical data. For the analysis of data utility in randomization model, we first investigate the accuracy analysis for associ- ation rule mining in market basket data. Then we propose a general framework to conduct theoretical analysis on how the randomization process affects the accuracy of various measures adopted in categorical data analysis. We also examine data utility when randomization mechanisms are not provided to data miners to achieve better privacy. We investigate how various objective associ- ation measures between two variables may be affected by randomization. We then extend it to multiple variables by examining the feasibility of hierarchical loglinear modeling. Our results provide a reference to data miners about what they can do and what they can not do with certainty upon randomized data directly without the knowledge about the original distribution of data and distortion information. Data privacy and data utility are commonly considered as a pair of conflicting re- quirements in privacy preserving data mining applications. In this dissertation, we investigate privacy issues in randomization models. In particular, we focus on the attribute disclosure under linking attack in data publishing. We propose efficient so- lutions to determine optimal distortion parameters such that we can maximize utility preservation while still satisfying privacy requirements. We compare our randomiza- tion approach with l-diversity and anatomy in terms of utility preservation (under the same privacy requirements) from three aspects (reconstructed distributions, accuracy of answering queries, and preservation of correlations). Our empirical results show that randomization incurs significantly smaller utility loss

Privacy-preserving data mining

In the research of privacy-preserving data mining, we address issues related to extracting knowledge from large amounts of data without violating the privacy of the data owners. In this study, we first introduce an integrated baseline architecture, design principles, and implementation techniques for privacy-preserving data mining systems. We then discuss the key components of privacy-preserving data mining systems which include three protocols: data collection, inference control, and information sharing. We present and compare strategies for realizing these protocols. Theoretical analysis and experimental evaluation show that our protocols can generate accurate data mining models while protecting the privacy of the data being mined

Protecting Privacy When Releasing Search Results from Medical Document Data

Health information technologies have greatly facilitated sharing of personal health data for secondary use, which is critical to medical and health research. However, there is a growing concern about privacy due to data sharing and publishing. Medical and health data typically contain unstructured text documents, such as clinical narratives, pathology reports, and discharge summaries. This study concerns privacy-preserving extraction, summary, and release of information from medical documents. Existing studies on privacy-preserving data mining and publishing focus mostly on structured data. We propose a novel approach to enable privacy-preserving extract, summarize, query and report patients’ demographic, health and medical information from medical documents. The extracted data is represented in a semi-structured, set-valued data format, which can be stored in a health information system for query and analysis. The privacy preserving mechanism is based on the cutting-edge idea of differential privacy, which offers rigorous privacy guarantee

Efficient distributed privacy preserving clustering

With recent growing concerns about data privacy, researchers have focused their attention to developing new algorithms to perform privacy preserving data mining. However, methods proposed until now are either very inefficient to deal with large datasets, or compromise privacy with accuracy of data mining results. Secure multiparty computation helps researchers develop privacy preserving data mining algorithms without having to compromise quality of data mining results with data privacy. Also it provides formal guarantees about privacy. On the other hand, algorithms based on secure multiparty computation often rely on computationally expensive cryptographic operations, thus making them infeasible to use in real world scenarios. In this thesis, we study the problem of privacy preserving distributed clustering and propose an efficient and secure algorithm for this problem based on secret sharing and compare it to the state of the art. Experiments show that our algorithm has a lower communication overhead and a much lower computation overhead than the state of the art

A Toolbox for privacy preserving distributed data mining

Distributed structure of individual data makes it necessary for data holders to perform collaborative analysis over the collective database for better data mining results. However each site has to ensure the privacy of its individual data, which means no information is revealed about individual values. Privacy preserving distributed data mining is utilized for that purpose. In this study, we try to draw more attention to the topic of privacy preserving data mining by showing a model which is realistic for data mining, and allows for very efficient protocols. We give two protocols which are useful tools in data mining: a protocol for Yaoѫs millionaires problem, and a protocol for numerical distance. Our solution to Yaoѫs millionaires problem is of independent interest since it gives a solution which improves on known protocols with respect to both computation complexity and communication overhead. This protocol can be used for different purposes in privacy preserving data mining algorithms such as comparison and equality test of data records. Our numerical distance protocol is also applicable to variety of algorithms. In this study we applied our numerical distance protocol in a privacy preserving distributed clustering protocol for horizontally partitioned data. We show application of our protocol over different attribute types such as interval-scaled,binary, nominal, ordinal, ratio-scaled, and alphanumeric. We present proof of security of our protocol, and explain communication, and computation complexity analysis indetail