Minimality attack in privacy preserving data publishing vldb. In the wellknown kanonymity model and the related models such as ldiversity and. Most literature on privacy preserving data publishing in a single provider setting considers only such attacks 2. Recent studies consider cases where the adversary may possess different. Uncertainty for anonymity in moving objects databases. Data publishing generates much concern over the protection of individual privacy. Citeseerx minimality attack in privacy preserving data. Introduction to privacy preserving data publishing. Preserving privacy while publishing data is an important requirement in many practical applications. In this paper, we introduce a model called mconfidentiality which deals with minimality attacks, and propose a feasible. Various privacy models have been used for data publishing and different attacks have been studied in privacy preserving data publishing, including minimality attacks 207, definettis theorem. Composition attacks, auxiliary information in data privacy. A study on performance analysis of privacy preservation data. Data privacy against composition attack springerlink.
Citeseerx document details isaac councill, lee giles, pradeep teregowda. In this paper, we call attacks based on some knowledge about algorithm and external knowledge an attack by mechanism. Recent studies consider cases where the adversary may. Our component analysis based privacy preserving data publishing ppdp mechanisms add noise, mostly on the. In contrast, privacypreserving data publishing ppdp may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. This is true for both global and local recoding and for the cases when the set of individuals related to te is a superset of that related to t. Ppdp studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Information about individuals andor organizations are collected from various sources which are being published, after applying some kinds preprocessing logic, that may lead to leaking sensitive information of individual. Unlike traditional attack s method in privacy preserving data publishing ppdp, mechanismbased attacks caused by the mechanism of a data publishing algorithm. Successful table linkage attack would reveal whether or not victim is present in data release. Slicing has several advantages when compared with generalization and bucketization. We propose a novel technique for publishing heterogeneous health data that provides an.
Differentialprivate data publishing through component analysis. Many data sharing scenarios require data to be anonymized. On minimality attack for privacypreserving data publishing. Analyzing mechanismbased attacks in privacypreserving. In this study, we investigate data privacy and utility. Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing signi. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Proceedings of the acm international conference on knowledge discovery, data mining.
Given a data set, priv acy preserving data publishing can b e in tuitively thought of as a game among four parties. If a table t is anonymized to t which satis es ldiversity, it can su er from a minimality attack. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on agreements on the use of published data. This is to certify that the thesis work entitled on minimality attack in privacy pre serving data publishing has been carried out by k. We are witnessing a continuous expansion of information technology that never ceases to impress us with its computational power, storage capacity, and agile mobility. Yet achieving the optimal balance between data privacy and utility needs has been documented as an nphard challenge 1 2. However, such an approach to data publishing is no longer applicable in shared multitenant cloud scenarios where users often have different levels of access to the same data. Several attack strategies have been proposed in the literature, which model the reasonings of an attacker and its. External table available to the adversary name qid andre q1 kim q1 jeremy q2 victoria q2 ellen q2 sally q2 ben q2 qid q1 q1 q2 q2 q2 q2 q2 name qid andre q1 kim q1 jeremy q2 victoria q2 ellen q2 sally q2 ben q2 tim q4 joseph q4 qid q1 q1 q2 q2 q2 q2 q2 q4 q4 a individual qid b multiset c individual qid d multiset. The need for sharing health data among multiple parties has become evident in several applications,1 such as decision support, policy development, and data mining. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. Pdf privacypreserving data publishing researchgate.
Duplication with trapdoor sensitive attribute values. In the attack of record linkage, some value qid on qid identifies a small number of. In this paper, we show that knowledge of the mechanism or algorithm of anonymization for data publication can also lead to extra. Minimality attack in privacy preserving data distributing mask eliminates the earlier discovered algorithm in the form of disclosure, but suffers from an additional type of algorithm in the form of disclosure which is solved by asap. Privacypreserving data publishing computing science simon. The following example illustrates a minimality attack on. Recent studies consider cases where the adversary may possess different kinds of knowledge about the data. We presented our views on the difference between privacypreserving data publishing and privacypreserving data mining, and gave a list of desirable properties of a privacypreserving data. Pdf attacks on anonymizationbased privacypreserving. Minimality attack in privacy preserving data publishing rcw wong, awc fu, k wang, j pei proceedings of the 33rd international conference on very large data bases, 2007. Gpsequipped devices, smart card automated fare collection systems, and sensory technology are but a few examples of. Privacypreserving data publishing semantic scholar.
Such technology is becoming more pervasive by the day and has enhanced various aspects of our daily lives. The current practice primarily relies on policies and guidelines to restrict the types of publishable data and on agreements on the use and storage of sensitive data. Asp is susceptible to algorithm in the form of disclosure. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on. The problem of data privacy perhaps goes back to 2000 when rakesh agrawal and ramakrishnan srikant published their seminal paper on privacypreserving data mining 1.
A new approach to privacy preserving data publishing. An investigation of data privacy and utility preservation. Every data publishing scenario in practice has its own assumptions and requirements on the data publisher, the data recipients, and the data publishing purpose. In this paper, we propose a novel technique for privacypreserving data publishing that provides andi.
The availability of data, however, often causes major privacy threats. Towards a privacy preserving framework for publishing. Privacy preserving data publishing seminar report and ppt. The general objective is to transform the original data into some anonymous form to prevent from inferring its record owners sensitive information. Preserving privacy in highdimensional data publishing. This is true for both global and local recoding and for the cases when the set of individuals related to te is a. We call such an attack based on the minimality principle a minimality attack.
A practical framework for privacypreserving data analytics. Along with the di erential privacy, generalization and suppression of attributes is applied to impose privacy and to prevent reidenti cation of records of a data set. Privacypreserving heterogeneous health data sharing. A study on performance analysis of privacy preservation. There is a trade of between data utility and privacy, if data utility is high then privacy is low and vice versa. Procedia technology 6 2012 970 a 977 22120173 2012 the authors. Uring the process of data privatization, the utility or usefulness of the privatized data diminishes. Privacy preserving data sanitization and publishing. A chore task is to develop methods which publish data in a.
While protecting privacy is a critical element in data publishing, it is equally important to preserve the utility of the published data, since this is the primary reason for data release. Successful attribute linkage attack results in sensitive attribute of victim to be released. On minimality attack in privacy preserving data publishing. Meanwhile, major concerns have been raised about individual privacy in health data sharing. In this paper, we survey research work in privacypreserving data publishing. To deal with these privacy issues, data must be anonymized so that no sensitive information about individuals can be disclosed from published data while distortion is minimized to ensure usefulness of data in practice. Minimality attack in privacypreserving data distributing mask eliminates the earlier discovered algorithm in the form of disclosure, but suffers from an additional type of algorithm in the form of disclosure which is solved by asap. Anonymizationbased attacks in privacypreserving data. Antidiscrimination analysis using privacy attack strategies. Privacy preservation, data publishing, k anonymity, ldiversity, minimality attack. In proceedings of the 24th ieee international conference on data engineering icde, pages 376385, 2008. Taking the decision tree induction classifier as an example, we show that our sanitization algorithm can be effectively tailored for preserving information. Pdf minimality attack in privacy preserving data publishing.
Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. Minimality attack in privacy preserving data publishing core. Data anonymization has become a major technique in privacy preserving data publishing. A survey of privacy preserving data publishing using. A survey on methods, attacks and metric for privacy. Analyzing mechanismbased attacks in privacypreserving data. Note that the minimality attack was originally reported as an attack on. Recent work focuses on proposing different anonymity algorithms for varying data publishing scenarios to satisfy privacy requirements, and keep data utility at the same time. Anonymizationbased attacks in privacypreserving data publishing. In this paper, we propose a model called mcon dentiality which deals with the individual privacy issue with the consideration of minimality attacks. Privacy preserving data publishing seminar report and.
Differentially private data release for data mining. In this thesis, we address privacy concerns in publishing longitudinaldata. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. Recent work has shown that generalization loses considerable amount of information, especially for highdimensional data. Minimality attack in privacy preserving data publishing proceedings.
Though the problem of optimal mcon dentiality anonymization is nphard, we propose an algorithm which generates mcon dential data sets e. With the wide deployment of electronic health record systems, health data are being collected at an unprecedented rate. Our experiments show that minimality attacks are practical concerns on real. Privacypreserving data publishing ppdp provides methods and tools for publishing useful. While protecting privacy is a critical element in data publishing, it is equally important to preserve the utility of the published data because this is the primary reason for data release. 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. In this study, we investigate data privacy and utility preservation using knn machine learning classification as a gauge 4. In contrast, privacy preserving data publishing ppdp may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. Many of them adopt a weak or relaxed adversarial or bayesoptimal privacy notion 9 to protect against speci.
The kanonymity model was proposed for privacy preserving data publication. It preserves better data utility than generalization. Synthetic data are generated from the privacypreserving synopsis to answer any queries or build data mining algorithms without decreasing the level of privacy protection. First, we introduce slicing as a new technique for privacy preserving data publishing. Unlike traditional attacks method in privacypreserving data publishing ppdp, mechanismbased attacks caused by the mechanism of a data publishing algorithm. In this paper, we present a privacypreserving data publishing framework for. Data user, like the researchers in gotham cit y university.
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