NDLI: Optimizing PPDM in asynchronous sparse data using random projection

Content Provider	IEEE Xplore Digital Library
Author	Raja Kumar, R. Indumathi, J. Uma, G.V.
Copyright Year	2008
Description	Author affiliation: Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai ¿ 600025, Tamilnadu, India (Raja Kumar, R.; Indumathi, J.; Uma, G.V.)
Abstract	Privacy is fetching a progressively more imperative issue in several data-mining applications dealing with sensitive data especially in health care, security, financial, behavioral etc., Most of the existing techniques are managing a Secure Two-Party Computation model, where two parties, each having a private database, want to cooperatively conduct data-mining operations on the union of their data. The problem we are pinning down for Privacy Preserving Data Mining(PPDM), is how a data owner can release a version of its confidential data with guarantees that the original sensitive information cannot be re-identified while the analytic properties of the data are preserved. In this paper we work to investigate the leeway of using multiplicative random projection sparse matrices for privacy preserving data in datasets which gets incremented asynchronously over time from various sources. The data stream is asynchronous. This work proposes the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch allows us to estimate L2 (Euclidean) distances and dot products with high accuracy. We have also proposed a conceptual architecture for implementing the privacy preservation techniques especially the Sparse Random Projection Matrix technique in incremental data to improve the level of privacy protection. We have tested to see that the perturbed data still preserves certain statistical characteristics of the data as the original unperturbed data. At this juncture we have proposed a generic projection based sketch for incremental data stream which can be used not only for this application but also can be used for any other applications, which supports incremental data bases. We have traced the origin of PPDM, the definition of privacy preservation in data mining, and the implications of benchmark privacy doctrine in information detection and advocate a few policies for PPDM based on these privacy principles. These are vital for the development and deployment of methodological solutions. This will let vendors and developers to construct unyielding information reuse and integration (IRI) in PPDM. We pursue to capitalize on the reuse of PPDM information by crafting easy, affluent, and reusable knowledge depictions and accordingly investigates tactics for amalgamate this knowledge into heritage systems and make advances in the upcoming of PPDM.
Starting Page	410
Ending Page	415
File Size	429765
Page Count	6
File Format	PDF
ISBN	9781424426591
DOI	10.1109/IRI.2008.4583066
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Data privacy Data mining Privacy Databases Distance measurement Data models Sparse Matrix Asynchronous Data Stream Secure Data Mining Data Sets L2 (Euclidean) distances Performance Privacy Preserving Data Mining (PPDM) PPDM_IRI Random Projection
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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