Utilizing Noise Addition for Data Privacy, an Overview
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paper_title: Secure Data Management in Decentralized Systems

paper_content:

The field of database security has expanded greatly, with the rapid development of global inter-networked infrastructure. Databases are no longer stand-alone systems accessible only to internal users of organizations. Today, businesses must allow selective access from different security domains. New data services emerge every day, bringing complex challenges to those whose job is to protect data security. The Internet and the web offer means for collecting and sharing data with unprecedented flexibility and convenience, presenting threats and challenges of their own. This book identifies and addresses these new challenges and more, offering solid advice for practitioners and researchers in industry.
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paper_title: Minimality Attack in Privacy Preserving Data Publishing

paper_content:

Data publishing generates much concern over the protection of individual privacy. Recent studies consider cases where the adversary may possess different kinds of knowledge about the data. In this paper, we show that knowledge of the mechanism or algorithm of anonymization for data publication can also lead to extra information that assists the adversary and jeopardizes individual privacy. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. We call such an attack a minimality attack. In this paper, we introduce a model called m-confidentiality which deals with minimality attacks, and propose a feasible solution. Our experiments show that minimality attacks are practical concerns on real datasets and that our algorithm can prevent such attacks with very little overhead and information loss.
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paper_title: Secure Data Management in Decentralized Systems

paper_content:

The field of database security has expanded greatly, with the rapid development of global inter-networked infrastructure. Databases are no longer stand-alone systems accessible only to internal users of organizations. Today, businesses must allow selective access from different security domains. New data services emerge every day, bringing complex challenges to those whose job is to protect data security. The Internet and the web offer means for collecting and sharing data with unprecedented flexibility and convenience, presenting threats and challenges of their own. This book identifies and addresses these new challenges and more, offering solid advice for practitioners and researchers in industry.
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paper_title: Utility and privacy of data sources: Can Shannon help conceal and reveal information?

paper_content:

The problem of private information “leakage” (inadvertently or by malicious design) from the myriad large centralized searchable data repositories drives the need for an analytical framework that quantifies unequivocally how safe private data can be (privacy) while still providing useful benefit (utility) to multiple legitimate information consumers. Rate distortion theory is shown to be a natural choice to develop such a framework which includes the following: modeling of data sources, developing application independent utility and privacy metrics, quantifying utility-privacy tradeoffs irrespective of the type of data sources or the methods of providing privacy, developing a side-information model for dealing with questions of external knowledge, and studying a successive disclosure problem for multiple query data sources.
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paper_title: Composition attacks and auxiliary information in data privacy

paper_content:

Privacy is an increasingly important aspect of data publishing. Reasoning about privacy, however, is fraught with pitfalls. One of the most significant is the auxiliary information (also called external knowledge, background knowledge, or side information) that an adversary gleans from other channels such as the web, public records, or domain knowledge. This paper explores how one can reason about privacy in the face of rich, realistic sources of auxiliary information. Specifically, we investigate the effectiveness of current anonymization schemes in preserving privacy when multiple organizations independently release anonymized data about overlapping populations. 1. We investigate composition attacks, in which an adversary uses independent anonymized releases to breach privacy. We explain why recently proposed models of limited auxiliary information fail to capture composition attacks. Our experiments demonstrate that even a simple instance of a composition attack can breach privacy in practice, for a large class of currently proposed techniques. The class includes k-anonymity and several recent variants. 2. On a more positive note, certain randomization-based notions of privacy (such as differential privacy) provably resist composition attacks and, in fact, the use of arbitrary side information.This resistance enables "stand-alone" design of anonymization schemes, without the need for explicitly keeping track of other releases. We provide a precise formulation of this property, and prove that an important class of relaxations of differential privacy also satisfy the property. This significantly enlarges the class of protocols known to enable modular design.
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paper_title: On the Complexity of Optimal Microaggregation for Statistical Disclosure Control

paper_content:

Statistical disclosure control (SDC), also termed inference control two decades ago, is an integral part of data security dealing with the protection of statistical databases. The basic problem in SDC is to release data in a way that does not lead to disclosure of individual information (high security) but preserves the informational content as much as possible (low information loss). SDC is dual with data mining in that progress of data mining techniques forces official statistics to a continual improvement of SDC techniques: the more powerful the inferences that can be made on a released data set, the more protection is needed so that no inference jeopardizes the privacy of individual respondents’ numerical data. This paper deals with the computational complexity of optimal microaggregation, where optimal means yielding minimal information loss for a fixed security level. More specifically, we show that the problem of optimal microaggregation cannot be exactly solved in polynomial time. This result is relevant because it provides theoretical justification for the lack of exact optimal algorithms and for the current use of heuristic approaches.
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paper_title: The Boundary Between Privacy and Utility in Data Publishing

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We consider the privacy problem in data publishing: given a database instance containing sensitive information "anonymize" it to obtain a view such that, on one hand attackers cannot learn any sensitive information from the view, and on the other hand legitimate users can use it to compute useful statistics. These are conflicting goals. In this paper we prove an almost crisp separation of the case when a useful anonymization algorithm is possible from when it is not, based on the attacker's prior knowledge. Our definition of privacy is derived from existing literature and relates the attacker's prior belief for a given tuple t, with the posterior belief for the same tuple. Our definition of utility is based on the error bound on the estimates of counting queries. The main result has two parts. First we show that if the prior beliefs for some tuples are large then there exists no useful anonymization algorithm. Second, we show that when the prior is bounded for all tuples then there exists an anonymization algorithm that is both private and useful. The anonymization algorithm that forms our positive result is novel, and improves the privacy/utility tradeoff of previously known algorithms with privacy/utility guarantees such as FRAPP.
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paper_title: Security and privacy in online social networks: A survey

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Social networking becomes increasingly important due to the recent surge in online interaction. Social network analysis can be used to study the functioning of computer networks, information flow patterns in communities, and emergent behavior of physical and biological systems. In this paper, the mathematical formulation and computational models for security and privacy of social network data are discussed. Several possible ways for an attacker to attack are presented so that the mathematical formulation can take them into account. The metrics for measuring the amount of security and privacy in an online social network (OSN) are discussed so that we have an idea of how good a model is. Based on these current techniques and attack strategies, future directions of research are discussed.
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paper_title: A survey on data security in data warehousing: Issues, challenges and opportunities

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Data Warehouses (DWs) are the enterprise's most valuable assets in what concerns critical business information, making them an appealing target for malicious inside and outside attackers. Given the volume of data and the nature of DW queries, most of the existing data security solutions for databases are inefficient, consuming too many resources and introducing too much overhead in query response time, or resulting in too many false positive alarms (i.e., incorrect detection of attacks) to be checked. In this paper, we present a survey on currently available data security techniques, focusing on specific issues and requirements concerning their use in data warehousing environments. We also point out challenges and opportunities for future research work in this field.
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paper_title: Privacy Preservation in Data Mining Through Noise Addition

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Due to advances in information processing technology and storage capacity, nowadays huge amount of data is being collected for various data analyses. Data mining techniques, such as classification, are often applied on these data to extract hidden information. During the whole process of data mining the data get exposed to several parties and such an exposure potentially leads to breaches of individual privacy. This thesis presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis. Our proposal has two main advantages. Firstly, as also suggested by our experimental results the perturbed data set maintains the same or very similar patterns as the original data set, as well as the correlations among attributes. While there are some noise addition techniques that maintain the statistical parameters of the data set, to the best of our knowledge this is the first comprehensive technique that preserves the patterns and thus removes the so called Data Mining Bias from the perturbed data set. Secondly, re-identification of the original records directly depends on the amount of noise added, and in general can be made arbitrarily hard, while still preserving the original patterns in the data set. The only exception to this is the case when an intruder knows enough about the record to learn the confidential class value by applying the classifier. However, this is always possible, even when the original record has not been used in the training data set. In other words, providing that enough noise is added, our technique makes the records from the training set as safe as any other previously unseen records of the same kind. In addition to the above contribution, this thesis also explores the suitability of prediction accuracy as a sole indicator of data quality, and proposes technique for clustering both categorical values and records containing such values.
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paper_title: On the Security of Noise Addition for Privacy in Statistical Databases

paper_content:

Noise addition is a family of methods used in the protection of the privacy of individual data (microdata) in statistical databases. This paper is a critical analysis of the security of the methods in that family.
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paper_title: Microdata Protection through Noise Addition

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Microdata protection by adding noise is being discussed for more than 20 years now. Several algorithms were developed that have different characteristics. The simplest algorithm consists of adding white noise to the data. More sophisticated methods use more or less complex transformations of the data and more complex error-matrices to improve the results. This contribution gives an overview over the different algorithms and discusses their properties in terms of analytical validity and level of protection. Therefore some theoretical considerations are shown and an illustrating empirical example is given.
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paper_title: Differential privacy: A survey of results

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Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database. ::: ::: In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.
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paper_title: Does differential privacy protect terry gross' privacy?

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The concept of differential privacy was motivated through the example of Terry Gross' height in Dwork (2006). In this paper, we show that when a procedure based on differential privacy is implemented, it neither protects Terry Gross' privacy nor does it provide meaningful responses to queries. We also provide an additional illustration using income data from the US Census. These illustrations raise serious questions regarding the efficacy of using differential privacy based masking mechanism for numerical data.
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paper_title: Differential Privacy Under Fire

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Anonymizing private data before release is not enough to reliably protect privacy, as Netflix and AOL have learned to their cost. Recent research on differential privacy opens a way to obtain robust, provable privacy guarantees, and systems like PINQ and Airavat now offer convenient frameworks for processing arbitrary userspecified queries in a differentially private way. However, these systems are vulnerable to a variety of covertchannel attacks that can be exploited by an adversarial querier. ::: ::: We describe several different kinds of attacks, all feasible in PINQ and some in Airavat. We discuss the space of possible countermeasures, and we present a detailed design for one specific solution, based on a new primitive we call predictable transactions and a simple differentially private programming language. Our evaluation, which relies on a proof-of-concept implementation based on the Caml Light runtime, shows that our design is effective against remotely exploitable covert channels, at the expense of a higher query completion time.
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paper_title: Does differential privacy protect terry gross' privacy?

paper_content:

The concept of differential privacy was motivated through the example of Terry Gross' height in Dwork (2006). In this paper, we show that when a procedure based on differential privacy is implemented, it neither protects Terry Gross' privacy nor does it provide meaningful responses to queries. We also provide an additional illustration using income data from the US Census. These illustrations raise serious questions regarding the efficacy of using differential privacy based masking mechanism for numerical data.
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paper_title: Statistics: An Introduction Using R

paper_content:

Preface. Chapter 1. Fundamentals. Chapter 2. Dataframes. Chapter 3. Central Tendency. Chapter 4. Variance. Chapter 5. Single Samples. Chapter 6. Two Samples. Chapter 7. Statistical Modelling. Chapter 8. Regression. Chapter 9. Analysis of Variance. Chapter 10. Analysis of Covariance. Chapter 11. Multiple Regression. Chapter 12. Contrasts. Chapter 13. Count Data. Chapter 14. Proportion Data. Chapter 15. Death and Failure Data. Chapter 16. Binary Response Variable. Appendix 1: Fundamentals of the R Language. References and Further Reading. Index.
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paper_title: On the Security of Noise Addition for Privacy in Statistical Databases

paper_content:

Noise addition is a family of methods used in the protection of the privacy of individual data (microdata) in statistical databases. This paper is a critical analysis of the security of the methods in that family.
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paper_title: The Essential Physics of Medical Imaging

paper_content:

This renowned work is derived from the authors' acclaimed national review course ("Physics of Medical Imaging") at the University of California-Davis for radiology residents. The text is a guide to the fundamental principles of medical imaging physics, radiation protection and radiation biology, with complex topics presented in the clear and concise manner and style for which these authors are known. Coverage includes the production, characteristics and interactions of ionizing radiation used in medical imaging and the imaging modalities in which they are used, including radiography, mammography, fluoroscopy, computed tomography and nuclear medicine. Special attention is paid to optimizing patient dose in each of these modalities. Sections of the book address topics common to all forms of diagnostic imaging, including image quality and medical informatics as well as the non-ionizing medical imaging modalities of MRI and ultrasound. The basic science important to nuclear imaging, including the nature and production of radioactivity, internal dosimetry and radiation detection and measurement, are presented clearly and concisely. Current concepts in the fields of radiation biology and radiation protection relevant to medical imaging, and a number of helpful appendices complete this comprehensive textbook. The text is enhanced by numerous full color charts, tables, images and superb illustrations that reinforce central concepts. The book is ideal for medical imaging professionals, and teachers and students in medical physics and biomedical engineering. Radiology residents will find this text especially useful in bolstering their understanding of imaging physics and related topics prior to board exams. Features: new Four-color throughout; new Companion website with fully searchable text and images; basic line drawings help to explain concepts; comprehensive coverage of diagnostic imaging modalities; and superb writing style of the author team helps make a difficult subject approachable and engaging.
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Title: Utilizing Noise Addition for Data Privacy, an Overview

Section 1: Introduction
Description 1: This section introduces the challenge of protecting data privacy in statistical databases and outlines the need for noise addition as a method for enhancing confidentiality.

Section 2: Background
Description 2: This section explains key terms and concepts crucial to understanding noise addition, including data privacy and confidentiality, data de-identification, and the balance between data utility and privacy.

Section 3: Related Work
Description 3: This section surveys existing research and techniques in data privacy, emphasizing current state-of-the-art methods and highlighting the need for noise addition.

Section 4: Noise Addition
Description 4: This section describes noise addition perturbation methods, including how noise is added or multiplied to confidential attributes to provide data confidentiality.

Section 5: Additive Noise
Description 5: This section delves into the specifics of additive noise, including the mathematical expressions and examples of how stochastic noise is used to conceal data values.

Section 6: Multiplicative Noise
Description 6: This section details multiplicative noise, explaining how random numbers are generated and multiplied with original data to achieve noise addition.

Section 7: Logarithmic Multiplicative Noise
Description 7: This section explains a variation of multiplicative noise where a logarithmic transformation is applied to the data before adding noise.

Section 8: Differential Privacy
Description 8: This section examines differential privacy as a modern noise addition technique, describing its mechanism and the balance between privacy and utility it aims to achieve.

Section 9: Differential Privacy Pros and Cons
Description 9: This section discusses the advantages and disadvantages of differential privacy, particularly its impact on data utility and statistical properties.

Section 10: Statistical Background for Noise Addition
Description 10: This section provides a detailed statistical foundation for noise addition, covering mean, variance, standard deviation, covariance, and correlation.

Section 11: Signal Noise Ratio (SNR)
Description 11: This section explores the application of SNR in data perturbation using noise addition, aiming for optimal data utility while maintaining privacy.

Section 12: Illustration
Description 12: This section gives a practical example of data perturbation with noise addition, detailing the steps and providing graphical comparisons between the original and perturbed data.

Section 13: Conclusion
Description 13: This section summarizes the main points of the paper, emphasizing the trade-offs between privacy and utility in noise addition techniques and suggesting areas for future research.