Can the utility of anonymized data be used for privacy breaches. In this paper, we discuss the requirements that anonymized data should meet and propose a new data anonymization approach based on tradeoff between utility and privacy to resist probabilistic. Pdf anonymization with worstcase distributionbased. Composition attacks and auxiliary information in data. Mining background kno wledge for data anon ymization. Since the background knowledge distributions of records in bkseq dataset are close to each other, the background knowledgebased clustering generates one cluster for j. Ninghui li purdue university, in purdue researchgate. Security using anonymization and slicing open access journals. Mining background knowledge for data anonymization tiancheng li, ninghui li. The idea of data mining is that the more data that we have, the more knowledge that we will have 2.
We call the derived patterns from the published data the foreground knowledge. On the tradeoff between privacy and utility in data publishing. Knowledgeoriented applications in data mining intechopen. Differentially private data release for data mining noman mohammed concordia university montreal, qc, canada. We call the uncovered patterns the foreground knowledge which is implicitly inside the table in contrast to the background knowledge, studied by existing works 21, 17, 30, 27, which the adversary. Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policymaking activities. The kanonymity has gained high popularity in research circles. Composition attacks and auxiliary information in data privacy. International journal on uncertainty, fuzziness and knowledge based systems,10 5, 2002. Watson research center, yorktown heights, ny 10598, usa haixun wang microsoft research asia. Data publisher should guarantee the authenticity of data to be published whatever processing methods will be used. Pdf data mining with background knowledge from the web. Data anonymization, also known as data masking or data desensitization, is used to obfuscate or conceal any sensitive data about an individual, thus.
It fails in preventing the background knowledge and homogeneity attacks, suffers from attribute linkage and record linkage, long. This is in contrast to the background knowledge that the adversary may obtain from other channels as studied in some previous work. For example, data mining and data analysis do not increase access to private data. Data mining is information process that extracts trusted and efficient knowledge form massive data sources. It should be noted that yaxis is a logarithmic scale. The solution encoding is then utilized to do anonymization for data publishing. Based on this rationale, we propose the injector frame w ork for data anon ymization. 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. Data mining with background knowledge from the web heiko paulheim, petar ristoski, evgeny mitichkin, and christian bizer university of mannheim data and web science group abstract many data mining problems can be solved better if more background knowledge. Knowledge mining definition of knowledge mining by the free. Efficient anonymization algorithms to prevent generalized. In this paper we present a method for reasoning about privacy using the concepts of exchangeability and definettis theorem.
Mining background knowledge for data anonymization, 2008 ieee 24th international conference on data engineering, cancun, pp. Thus, randomization and perturbation cannot meet the requirements in this scenario. Although more research is necessary before it is ready for production use, data anonymization can ease some security concerns, allowing for simpler demilitarized zone and security. Theresa beaubouef, southeastern louisiana university abstract the world is deluged with various kinds of data scientific data, environmental data, financial data and mathematical data. It is usually an arduous task to process and integrate all the knowledge needed for model construction.
We propose a bucketizationbased technique, entitled k, lclustering to prevent such privacy breaches by ensuring that the same k individuals remain grouped together over the entire anonymized stream. We apply our deanonymization methodology to the net. Data mining with background knowledge from the web. Privacy preservation in data mining using anonymization technique. Leverage large volumes of multistructured data for advanced data mining and predictive. Based on the practical assumption that an adversary has only limited background knowledge on a target victim, we adopt k, c lprivacy model for trajectory data anonymization, which takes into consideration not only identity linkage attacks on trajectory data, but also attribute linkage attacks via trajectory data. Data mining and data analysis certainly can make private data more useful, but they can only operate on data that is already accessible. Concepts, background and methods of integrating uncertainty in data mining yihao li, southeastern louisiana university faculty advisor. In this paper, we study the problem of publishing setvalued data for data mining tasks under the rigorous di. For ldiversity, the anonymization conditions are satis. Aug 22, 2014 in this chapter, we describe the attribute disclosure underlying scenario, the different forms of background knowledge of the adversary the adversary may have and their potential privacy attacks. Sep, 2014 major issues in data mining mining methodology mining different kinds of knowledge from diverse data types, e. Injector mines negative association rules from the data to be released and uses them in the anonymization process.
Page 2 so a common practice is for organizations to release and receive personspecific data. We also develop an efficient anonymization algorithm to compute the injected tables that incorporates background knowledge. Modeling and integrating background knowledge in data anonymization. Preservation in highdimensional data using anonymization. It aims at extracting unknown but useful knowledge from. Data mining is a process of discovering interesting knowledge from large amounts of data stored either, in database, data warehouse, or other information repositories 2. Data mining is the process of extracting interesting patterns or knowledge from huge amount of data. Concepts, background and methods of integrating uncertaint y in data m ining yihao li, southeastern louisiana university faculty advisor.
In the meanwhile, they reduce the utility of the data. There are many tools, technologies, and methodologies that can be used to reverse engineer or deanonymize data. Data mining the analysis step of the knowledge discovery in databases process, or kdd, a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. A brief survey on anonymization techniques for privacy. Another approach called injector uses data mining to model background knowledge of a possible attacker 11 and then optimizes the anonymization based on this background knowledge. An encryption scheme, known as rob frugal is proposed. Data anonymization can also be considered by covered entities that are leveraging data driven research analysis projects e. Data mining with background knowledge from the web heiko paulheim, petar ristoski, evgeny mitichkin, and christian bizer university of mannheim data and web science group abstract many data mining problems can be solved better if more background knowledge is added. Cmixture and multiconstraints based genetic algorithm for collaborative data publishing. The kanonymity privacy requirement for publishing microdata requires that each equivalence class i. A study on kanonymity, l diversity, and tcloseness.
An attack model is developed based on the background knowledge for privacy preserving outsourced mining. Injector mines negati ve association rules from the data to be released and uses them in the anonymization pr ocess. Anonymization of the data is done by hiding the identity of record owners, whereas privacy preserving data mining seeks to directly belie the sensitive data. These studies propose a language for expressing background knowledge and analyze the disclosure risk when the adversary has a certain amount of knowledge in the language. The extraction of useful, often previously unknown information from large databases or data sets. Cmixture and multiconstraints based genetic algorithm.
Knowledge mining synonyms, knowledge mining pronunciation, knowledge mining translation, english dictionary definition of knowledge mining. Pdf parallelizing kanonymity algorithm for privacy. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It has been shown that checking perfect privacy zero information disclosure, which applies to measuring differ. Experimental results show that injector reduces privacy risks against background knowledge attacks while improving data utility. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information with intelligent methods from a data set and transform the information into a comprehensible structure for. Differentially private data release for data mining. Big data management and security chapters site home. Another myth is that data mining and data analysis require masses of data.
However, it is important to point out the risks associated with these types of efforts. Meyyappan, anonymization technique through record elimination to preserve privacy of published data. In recent years, there has been a tremendous growth in the amount of personal data that can be collected and analyzed by the organizations 1. Meyyappan, anonymization technique through record elimination to preserve privacy of published data, 20 international conference on pattern. Data mining is also known as knowledge discovery in databases kdd which is the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. This is in contrast to the background knowledge that the adversary may obtain from other channels.
Modeling and integrating background knowledge in data. In fact, one of the purposes of data publishing is for data mining which is mainly about the discovery of patterns from the published data. Aggarwal jianyong wang abstract the problem of privacypreserving data mining has attracted considerable attention in recent years because of increasing concerns about the privacy of the underlying data. Encryption anonymization data should be natively encrypted during ingestion of data into hadoop regardless of the data.
In recent years, due to increase in ability to store personal data about users and the increasing sophistication of data mining algorithms to leverage this information the problem of privacy preserving data mining has become more important. Though it has some drawbacks and other ppdm algorithms such as ldiversity, tcloseness and mprivacy came into existence, the anonymization. Secondly, we show how an adversary can breach privacy by computing the probability that an individual is linked to a sensitive value by using foreground knowledge. Hierarchical anonymization algorithms against background. In addition to understanding each section deeply, the two books present useful hints and strategies to solving. Once again, the antidiscrimination analyst is faced with a large space of. A myriad of data mining algorithms with high complexity. Anonymization with worstcase distributionbased background knowledge. This document, protection of personal data in clinical documents a model approach, is an update of clinical study reports approach to protection of personal data 5 that reflects the. We then present the injector framework for data anonymization. Li, on the tradeoff between privacy and utility in data publishing, in.
Paper in pdf mining roles with semantic meanings ian molloy, hong chen, tiancheng li, qihua wang, ninghui li, elisa bertino, seraphin carlo, and jorge lobo in proceedings of the acm symposium on access control models and technologies sacmat, pp. A survey of privacy preserving data publishing using. Mining background knowledge for data anonymization. We illustrate the usefulness of this technique by usin. These studies propose a language for expressing background knowledge and analyze the disclosure risk when the adversary has a. In proceedings of the international conference on data engineering icde. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversarys background knowledge. Pdf background knowledge is an important factor in privacy preserving data publishing. In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection.
W e then pr esent the injector framew ork for data anonymization. Classification and analysis of anonymization techniques. The analysis, however, is unaware of the exact background knowledge possessed by the adversary. Center for education and research information assurance and security purdue university, west lafayette, in 479072086.
Suppose a table t is to be anonymized for publication. Finally, this paper examines the security issues in big data and compares various anonymization. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. Mining background kno wledge for data anon ymization t iancheng li, ninghui li. The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject.
In this paper, we address the correlation problem in the anonymization of transactional data streams. Another approach called injector uses data mining to model background knowledge of a possible attacker 16 and then optimizes the anonymization based on this background knowl edge. All existing data publishing methods for setvalued data are based on partitionbased privacy models, for example kanonymity, which are vulnerable to privacy attacks based on background. In recent years, there has been a tremendous growth in the amount of personal data that. Decision model construction is a knowledge intensive task, involving one or more decision analysts working closely with one or more domain experts to elicit the relevant structural and numerical parameters of the decision models. It is important to consider the tradeoff between privacy and utility. I n itia l m ic r o d a ta n a m e a g e d ia g n o s is i n c o m e. A deep learning approach for privacy preservation in. An anonymization method based on tradeoff between utility and. One intriguing aspect of our approach is that one can argue that it improves both privacy and utility at the same time, as it both protects against background knowledge attacks and better preserves the features in the data. The series of books entitled by data mining address the need by presenting indepth description of novel mining algorithms and many useful applications. The data mining community enjoyed revival after samarti and sweeney proposed k anonymization for privacy preserving data mining.
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