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Data Shuffling for Protecting Confidential Data

Data Shuffling for Protecting Confidential Data. A Software Demonstration. Rathindra Sarathy * and Krish Muralidhar ** * Oklahoma State University, Stillwater, OK 74078 USA (rathin.sarathy@okstate.edu) ** University of Kentucky, Lexington, KY 40506, USA (krishm@uky.edu).

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Data Shuffling for Protecting Confidential Data

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  1. Data Shuffling for Protecting Confidential Data A Software Demonstration • RathindraSarathy* and KrishMuralidhar** • * Oklahoma State University, Stillwater, OK 74078 USA (rathin.sarathy@okstate.edu) • ** University of Kentucky, Lexington, KY 40506, USA • (krishm@uky.edu)

  2. Data Shuffling • Method that: • Combines the strengths of data perturbation and data swapping • Shuffled data uses only original confidential values – no added noise or “unreasonable” values • Preserves marginals exactly and all monotonic relationships among variables (preserves pairwise rank order correlation closely) • Non-parametric method • Maximum protection against identity and value disclosure

  3. Technical Basis • X represents M confidential variables • S represents L non-confidential variables. • X is assumed numerical; S can be categorical or numerical variables. • Y represents the masked values of X. • Let R is rank order correlation matrix of {X, S}. Define variables as follows: X* and S* as:

  4. Technical Basis - continued

  5. Example Dataset – Shuffled data

  6. Example Dataset – Rank Order Correlations

  7. Example Dataset - Relationships

  8. Software Demo • Two versions are available – both in early Beta (Java and Windows) • Example shown earlier was run on Java version of software • We will demonstrate the software later

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