Sop slicing
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Chirag Ramesh Desai proposes enhancing existing anonymization techniques for privacy-preserving data publishing. Existing techniques like generalization and bucketization have drawbacks like loss of information and inability to prevent membership disclosure. Slicing breaks attribute associations but can reveal tuples when similar values are present. Desai aims to design an enhanced slicing model using suppression slicing to suppress attribute values or Mondrian slicing with random permutation across buckets, addressing slicing's drawbacks while maintaining data utility. The goal is to overcome limitations of existing techniques and enhance privacy in published data.
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Sop slicing
- 1. Statement Of Purpose 1) A New Approach for Privacy Preserving Data Publishing By: Chirag Ramesh Desai (ME COMPS) 1) INTRODUCTION :- Privacy preservation in publishing of micro data has been studied extensively in recent years. Micro data contain records each of which contains information about an individual entity, such as a person, a household, or an organization. Several anonymization techniques, such as generalization, bucketization and slicing have been designed for privacy preserving micro data publishing. 2) Objective Of Project :- To overcome drawback of anonymization techniques .And to enhance the either of the anonymization techniques like generalization, bucketization and slicing for preserving the privacy in data publishing . 3) Motivation :- Slicing model partitions the data set both vertically and horizontally. The basic idea of slicing is to break the association cross columns, but to preserve the association within each column. This reduces the dimensionality of the data and preserves better utility than generalization and Bucketization. Slicing protects privacy because it breaks the associations between uncorrelated attributes, which are Infrequent . Slicing has the drawback that when more number of similar attribute value and the sensitive value are present, different tuples may give the original tuple while performing the random permutation. The utility of the dataset is also lost by generating the fake tuples. Thus enhanced slicing model has to be design to overcome the drawbacks of slicing. SOP-1 Page 1
- 2. Statement Of Purpose 1) A New Approach for Privacy Preserving Data Publishing By: Chirag Ramesh Desai (ME COMPS) 4) Limitation Of Existing System :- ? Generalization has the drawback that it loses considerable amount of information, especially for high dimensional data. ? Bucketization has the drawback that it does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. ? Slicing has the drawback that when more number of similar attribute value and the sensitive value may present, different tuples may give the original tuple while performing the random permutation. The utility of the dataset is also lost by generating the fake tuples. 5) Proposed novel improvement or techniques :- To design or implement enhanced slicing model for preserving privacy in data publication by using Suppression slicing and/or Mondrian slicing. 6) Intended Methodology :- The Suppression slicing will suppress any one of the attribute value in the tuples and then perform the slicing. In Mondrian slicing random permutation is done with all the buckets not within the single bucket. 7) Project scope :- To overcome drawback of slicing technique is the main scope. The suppression slicing will help to maintain utility with minimum loss by suppressing only very few values and privacy is maintained by random permutation. In Mondrian slicing random permutation is done that will help to maintain same utility of the original dataset is maintained. SOP-1 Page 2