User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

A Modified Anonymisation Algorithm Towards Reducing Information Loss.

The growth of various technologies in the modern digital world results in the col- lection and storage of huge amounts of individual\u27s data. In addition of providing direct services delivery, this data can be used for other non-direct activities known as secondary use. This includes activities such as doing research, analysis, quality and safety measurement, public health, and marketing

Feature Based Data Anonymization for High Dimensional Data

Information surges and advances in machine learning tools have enable the collection and storage of large amounts of data. These data are highly dimensional.  Individuals are deeply concerned about the consequences of sharing and publishing these data as it may contain their personal information and may compromise their privacy. Anonymization techniques have been used widely to protect sensitive information in published datasets. However, the anonymization of high dimensional data while balancing between privacy and utility is a challenge. In this paper we use feature selection with information gain and ranking to demonstrate that the challenge of high dimensionality in data can be addressed by anonymizing attributes with more irrelevant features. We conduct experiments with real life datasets and build classifiers with the anonymized datasets. Our results show that by combining feature selection with slicing and reducing the amount of data distortion for features with high relevance in a dataset, the utility of anonymized dataset can be enhanced. Keywords: High Dimension, Privacy, Anonymization, Feature Selection, Classifier, Utility DOI: 10.7176/JIEA/9-2-03 Publication date: April 30th 201

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