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Training a classifier requires accessing a large collection the privacy rights of the individual . Suppose that the of data. Even after removing explicit identifying information such as Name be released say, to some health research institute for research and SSN, it is still possible to link released records back to purposes. A useful approach to combat such linking attacks, called k-anonymization , is 1 If there is a taxonomical description for a categorical anonymizing the linking attributes so that at least k released attribute e.
For example, we can generalize that minimizes some data distortion metric. We argue that the cities San Francisco, San Diego, and Berkeley into minimizing the distortion to the training data is not relevant to the classification goal that requires extracting the structure of the corresponding state California.
We conducted intensive experiments to evaluate the impact of anonymization on the classification on keeping Berkeley. Experiments on real life data show that the quality 3 If the attribute is a continuous attribute e. For example, we can replace Index Terms— Privacy protection, anonymity, security, in- specific Birthyear values from to with an tegrity, data mining, classification, data sharing interval [ By applying such masking operations, the information on I.
An example in Samarati  shows that linking person tends to match more records. For example, a male born in San Francisco in will match all records that have the values hCA, [ , Mi; clearly not all matched records medication records with a voter list can uniquely identify a correspond to the person. New privacy acts it more difficult to tell whether an individual actually has the and legislations are recently enforced in many countries.
In diagnosis in the matched records. Making the released data and Electronic Document Act  to protect a wide spectrum useful to data analysis is another goal. In this paper, we of information, e. The next example shows intentions to acquire goods or services. Government protected while preserving the usefulness for classification. Table I and taxonomy trees in Figure 1.
Each row represents one or more records Birthplace, Birthyear, Sex, and Diagnosis. Semantically, this B. Fung and K. Yu is with the IBM T. Watson Research Center, 19 Skyline Drive, rows with each row representing one record. There is only Hawthorne, NY Email: psyu us. Junior Sec. Senior Sec. Taxonomy trees and QIDs Total: 21Y13N 34 prefer interpretability, or some prefers recall while the others the person represented uniquely distinguishable from others by prefer precision, and so on.
In other cases, the recipient may Sex and Education. Publishing the one of the four females with a graduate school degree. As data provides the recipient a greater flexibility of data analysis. To construct distinction of M asters and Doctorate. The data training data and is used to classify the future data.
For example, if any of important that the classifier makes use of the structure that Education and Work Hrs is sufficient for determining the class will repeat in the future data, not the noises that occur only and if one of them is distorted, the class can still be determined in the training data.
In Table I, 19 out of 22 persons having from the other attribute. It is not likely that this without compromising the quality of classification. To this end, difference is entirely due to sampling noises.
In contrast, M we propose an information metric to focus masking operations and F of Sex seem to be arbitrarily associated with both on the noises and redundant structures. Below are several useful features In this paper, we consider the following k-anonymization of our approach. Two types of information refine masked values top-down starting from the most in the table are released.
The first type is sensitive infor- masked table. The quasi-identifier does without taxonomy, and continuous attributes. Compared to the sin- the combination is unique. The data provider wants to prevent gle quasi-identifier that contains all the attributes, we linking the released records to an individual through the quasi- enforce k-anonymity on only attribute sets that can be identifier. This privacy requirement is specified by the k- potentially used as an quasi-identifier.
This approach anonymity : if one record in the table has some value on avoids unnecessary distortion to the data. Our method has a linear The k-anonymization for classification is to produce a masked time complexity in the table size. Moreover, the user can table that satisfies the k-anonymity requirement and retains stop the top-down refinement any time and have a table useful information for classification.
A formal statement will satisfying the anonymity requirement. The notion of k-anonymity was first proposed in . In If classification is the goal, why does not the data provider general, a cost metric is used to measure the data distor- build and publish a classifier instead of publishing the data?
Two types of cost metric have been There are real-life scenarios where it is necessary to release the considered. The first type, based on the notion of minimal data. First of all, knowing that the data is used for classification generalization  , is independent of the purpose of the does not imply that the data provider knows exactly how data release. The second type factors in the purpose of the the recipient may analyze the data.
The recipient often has data release such as classification . The goal is to find the application-specific bias towards building the classifier.
For optimal k-anonymization that minimizes this cost metric. Greedy methods were proposed in      to protect against linking an individual to sensitive information .
Scalable algorithms with the exponential complexity either within or outside T through some identifying attributes, the worst-case for finding the optimal k-anonymization were called a quasi-identifier or simply QID. A sensitive linking studied in    . This requirement is formally defined able to classification where masking structures and masking below. It is well identifiers QID1 ,. The ified data, which has the lowest possible cost according to anonymity of QIDi , denoted A QIDi , is the smallest a qidi any cost metric, often has a worse classification than some for any value qidi on QIDi.
A table T satisfies the anonymity generalized i. This observation was confirmed by our experiments. The specified by the data provider. Following a similar argument, error on future data.
This requirement is violated by allow the co-existence of a specific value and a general value, h9th, M i, hM asters, F i, hDoctorate, F i. To protect linking such as Bachelor and U niversity in Table I. To prevent linking through analysis phase. Suppose that the data provider wants to release a classify bachelor. In this case, enforcing provider. All previous works suffer from  limited the breach probability, which is similar to the this problem because they handled multiple QIDs through the notion of confidence, and allowed a flexible threshold for single QID made up of all attributes in the multiple QIDs.
A leaf node represents a domain value A data provider wants to release a person-specific table and a parent node represents a less specific value. Figure T D1 ,. A generalized label Class. A record has the form hv1 ,. The data provider also wants leaf path. The suppression of a value on Dj means replacing all occurrences of the value with the special III. Our is at the value level in that Supj is in general a subset method iteratively refines a masked value selected from the of the values in the attribute Dj.
The lating the anonymity requirement. Each refinement increases discretization of a value v on Dj means replacing all the information and decreases the anonymity since records occurrences of v with an interval containing the value. More details will be discussed in Section III. Definition 2 Anonymity for Classification : Given a table Refinement for generalization.
Refinement for suppression. It does not disclosing one value v from the set of suppressed values Supj. For this reason, our Refinement for discretization.
Privacy preservation techniques in big data analytics: a survey
Classification of data with privacy preservation is a fundamental problem in privacy preserving data mining. The privacy goal requires concealing the sensitive information that may identify certain individuals breaching their privacy, whereas the classification goal requires to accurately classifying the data. One way to achieve both is to anonymize the dataset that contains the sensitive information of individuals before getting it released for data analysis. Microaggregation is an efficient privacy preservation technique used by statistical disclosure control community as well as data mining community to anonymize a dataset. It naturally satisfies k -anonymity without resorting to generalisations or suppression of data. In MiCT method data are perturbed prior to its classification and we use tree properties to achieve the objective of privacy preserving classification of data. To evaluate the effectiveness of the proposed method we have conducted experiments on real life data and proved that our method provides improved classification accuracy by preserving privacy.
Anonymizing Classification Data for Preserving Privacy
Metrics details. Incredible amounts of data is being generated by various organizations like hospitals, banks, e-commerce, retail and supply chain, etc. Not only humans but machines also contribute to data in the form of closed circuit television streaming, web site logs, etc. Tons of data is generated every minute by social media and smart phones. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However data analytics is prone to privacy violations.