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Papers by rahul patil

Research paper thumbnail of Balancing Quality and Confidentiality for Multivariate Tabular Data

Absolute cell deviation has been used as a proxy for preserving data quality in statistical discl... more Absolute cell deviation has been used as a proxy for preserving data quality in statistical disclosure limitation for tabular data. However, users’ primary interest is that analytical properties of the data are for the most part preserved, meaning that the values of key statistics are nearly unchanged. Moreover, important relationships within (additivity) and between (correlation) the published tables should also be unaffected. Previous work demonstrated how to preserve additivity, mean and variance in for univariate tabular data. In this paper, we bridge the gap between statistics and mathematical programming to propose nonlinear and linear models based on constraint satisfaction to preserve additivity and covariance, correlation, and regression coefficient between data tables. Linear models are superior than nonlinear models owing to simplicity, flexibility and computational speed. Simulations demonstrate the models perform well in terms of preserving key statistics with reasonable accuracy.

Research paper thumbnail of Computational Aspects of Controlled Tabular Adjustment: Algorithm and Analysis

Statistical agencies have used complementary cell suppression to limit statistical disclosure in ... more Statistical agencies have used complementary cell suppression to limit statistical disclosure in tabular data for four decades. Cell suppression results in significant information loss and reduces the usefulness of published data, significantly so for the unsophisticated user. Furthermore, computing optimal complementary cell suppressions is known to be an NP-hard problem. In this paper, we explore a recent method for limiting disclosure in tabular data, controlled tabular adjustment (CTA). Based on a mixed integer-programming model for CTA, we present a procedure that provides a lower bound on the objective, which is demonstrated to decrease the computational effort required to solve the model. We perform experiments to examine heuristics for CTA proposed elsewhere that can be used to convert the MIP problem into linear programming problems while preserving essential information.

Research paper thumbnail of Integrated exact, hybrid and metaheuristic learning methods for confidentiality protection

Annals of Operations Research, 2011

A vital task facing government agencies and commercial organizations that report data is to repre... more A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentiality of critical components of this data. The challenge is to organize and disseminate data in a form that prevents such critical components from being inferred by groups bent on corporate espionage, to gain competitive advantages, or having a desire to penetrate the security of the information underlying the data. Controlled tabular adjustment is a recently developed approach for protecting sensitive information by imposing a special form of statistical disclosure limitation on tabular data. The underlying model gives rise to a mixed integer linear programming problem involving both continuous and discrete (zero-one) variables. We develop stratified ordered (s-ordered) heuristics and a new meta-heuristic learning approach for solving this model, and compare their performance to previous heuristics and to an exact algorithm embodied in the state-of-the-art ILOG- CPLEX software. Our new approaches are based on partitioning the problem into its discrete and continuous components, first creating an s-ordered heuristic that reduces the number of binary variables through a grouping procedure that combines an exact mathematical programming model with constructive heuristics. To gain further advantages we then replace the mathematical programming model with an evolutionary scatter search approach that makes it possible to extend the method to large problems with over 9000 entries. Finally, we introduce a new metaheuristic learning method that significantly improves the quality of solutions obtained.

Research paper thumbnail of Exact, Heuristic and Metaheuristic Methods for Confidentiality Protection by Controlled Tabular Adjustment

Research paper thumbnail of Influences of Self-Assembled Structure on Mobilities of Charge Carriers in π-Conjugated Polymers

Journal of Physical Chemistry B, 2005

Research paper thumbnail of Influence of film structure on mobilities of charge carriers in conducting polymers

Electrochimica Acta, 2007

Research paper thumbnail of Mobilities of charge carriers in poly( o-methylaniline) and poly( o-methoxyaniline

Electrochimica Acta, 2004

Research paper thumbnail of Template-free Formation of Microspheres Based on Poly(N-methylaniline

Research paper thumbnail of Charge carriers in polyaniline film: a correlation between mobility and in-situ ESR measurements

Journal of Electroanalytical Chemistry, 2002

Research paper thumbnail of Balancing Quality and Confidentiality for Multivariate Tabular Data

Absolute cell deviation has been used as a proxy for preserving data quality in statistical discl... more Absolute cell deviation has been used as a proxy for preserving data quality in statistical disclosure limitation for tabular data. However, users’ primary interest is that analytical properties of the data are for the most part preserved, meaning that the values of key statistics are nearly unchanged. Moreover, important relationships within (additivity) and between (correlation) the published tables should also be unaffected. Previous work demonstrated how to preserve additivity, mean and variance in for univariate tabular data. In this paper, we bridge the gap between statistics and mathematical programming to propose nonlinear and linear models based on constraint satisfaction to preserve additivity and covariance, correlation, and regression coefficient between data tables. Linear models are superior than nonlinear models owing to simplicity, flexibility and computational speed. Simulations demonstrate the models perform well in terms of preserving key statistics with reasonable accuracy.

Research paper thumbnail of Computational Aspects of Controlled Tabular Adjustment: Algorithm and Analysis

Statistical agencies have used complementary cell suppression to limit statistical disclosure in ... more Statistical agencies have used complementary cell suppression to limit statistical disclosure in tabular data for four decades. Cell suppression results in significant information loss and reduces the usefulness of published data, significantly so for the unsophisticated user. Furthermore, computing optimal complementary cell suppressions is known to be an NP-hard problem. In this paper, we explore a recent method for limiting disclosure in tabular data, controlled tabular adjustment (CTA). Based on a mixed integer-programming model for CTA, we present a procedure that provides a lower bound on the objective, which is demonstrated to decrease the computational effort required to solve the model. We perform experiments to examine heuristics for CTA proposed elsewhere that can be used to convert the MIP problem into linear programming problems while preserving essential information.

Research paper thumbnail of Integrated exact, hybrid and metaheuristic learning methods for confidentiality protection

Annals of Operations Research, 2011

A vital task facing government agencies and commercial organizations that report data is to repre... more A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentiality of critical components of this data. The challenge is to organize and disseminate data in a form that prevents such critical components from being inferred by groups bent on corporate espionage, to gain competitive advantages, or having a desire to penetrate the security of the information underlying the data. Controlled tabular adjustment is a recently developed approach for protecting sensitive information by imposing a special form of statistical disclosure limitation on tabular data. The underlying model gives rise to a mixed integer linear programming problem involving both continuous and discrete (zero-one) variables. We develop stratified ordered (s-ordered) heuristics and a new meta-heuristic learning approach for solving this model, and compare their performance to previous heuristics and to an exact algorithm embodied in the state-of-the-art ILOG- CPLEX software. Our new approaches are based on partitioning the problem into its discrete and continuous components, first creating an s-ordered heuristic that reduces the number of binary variables through a grouping procedure that combines an exact mathematical programming model with constructive heuristics. To gain further advantages we then replace the mathematical programming model with an evolutionary scatter search approach that makes it possible to extend the method to large problems with over 9000 entries. Finally, we introduce a new metaheuristic learning method that significantly improves the quality of solutions obtained.

Research paper thumbnail of Exact, Heuristic and Metaheuristic Methods for Confidentiality Protection by Controlled Tabular Adjustment

Research paper thumbnail of Influences of Self-Assembled Structure on Mobilities of Charge Carriers in π-Conjugated Polymers

Journal of Physical Chemistry B, 2005

Research paper thumbnail of Influence of film structure on mobilities of charge carriers in conducting polymers

Electrochimica Acta, 2007

Research paper thumbnail of Mobilities of charge carriers in poly( o-methylaniline) and poly( o-methoxyaniline

Electrochimica Acta, 2004

Research paper thumbnail of Template-free Formation of Microspheres Based on Poly(N-methylaniline

Research paper thumbnail of Charge carriers in polyaniline film: a correlation between mobility and in-situ ESR measurements

Journal of Electroanalytical Chemistry, 2002

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