System and method for enabling differential privacy techniques
Existing systems provide data selection for one differential technique considering an analytical problem or synthetic data but not an arrangement for selection of one or more techniques together. The embodiments herein provide a method and system for differential privacy enabled service with hybrid rule management and similarity metrics to select data. The system generates a query table called universal data from the associates tables and databases. The system further based on query on the universal table of single columns or selected columns with different parameters using different privacy rules generates differential private data stored in temp tables/views. The system retrieves queried data of different techniques and parameters interactively viewing the privacy and similarity of each data types of retrieved data with universal data using different metrics like bar charts, Histograms, average, std. and correlation to select right different privacy data of the universal data based on Privacy and similarity tolerances.
1. A processor-implemented method comprising steps of:
receiving, via an input/output interface, one or more sensitivity parameters of a structured data, an epsilon value, a plurality of differential privacy techniques, and a privacy budget selected by a user;
fetching, via one or more hardware processors, the structured data from a predefined database to generate a production data for a differential privacy, wherein the structured data is labelled and in a tabular form;
profiling, via the one or more hardware processors, the production data based on type and nature of the structured data, wherein the type of the structured data includes a numerical form, a categorical form, a binary form, and in a text form, and the nature of the structured data includes continuous, discrete, integer, and Boolean;
creating, via the one or more hardware processors, based on the profiling of the production data, a staging data for analytical purpose;
selecting, via the one or more hardware processors, at least one differential privacy technique from the plurality of differential privacy techniques based on one or more sensitive data fields of the staging data, the epsilon value, the sensitivity parameters of the data, and the privacy budget, wherein the plurality of differential privacy techniques comprises a Laplace classic, a Laplace bounded, an exponential and a random toss;
applying, via the one or more hardware processors, the selected at least one differential privacy technique iteratively on the staging data and an incremental addition of epsilon value at each iteration, wherein the incremental addition of epsilon at each iteration is based on a privacy loss at each iteration that is less than the privacy budget, wherein each of the selected at least one differential privacy technique generates a privacy enabled structured data, and wherein the epsilon value and the sensitivity parameters of the data are used as levers to control a degree of noise being added to the sensitive data fields by the selected at least one differential privacy technique; and
enabling, via the one or more hardware processors, the user to select the differential privacy enabled structured data based on one or more results of the selected differential privacy technique application, wherein the one or more results include a set of privacy metrics providing information on privacy strength and similarity tolerance of each query in an interactive way.
2. The processor-implemented method of claim 1 , wherein one or more results comprise a histogram, a KDE Curve, a standard deviation, and a correlation for similarity strength.
3. The processor-implemented method of claim 1 , wherein the set of privacy metrics comprise of a privacy percentage, a privacy Digit match, and a privacy error.
4. The processor-implemented method of claim 3 , wherein the privacy percentage is calculated based on number of matches, a number of digits mismatch, and a distance difference.
5. A system comprising:
an input/output interface to receive one or more sensitivity parameters of a data, an epsilon value, a plurality of differential privacy techniques, and a privacy budget selected by a user;
one or more hardware processors;
a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory, to:
fetch the structured data from a predefined database to generate a production data for a differential privacy, wherein the structured data is labelled and in a tabular form;
profile the production data based on type and nature of the structured data, wherein the type of the structured data includes a numerical form, a categorical form, a binary form, and in a text form, and the nature of the structured data includes continuous, discrete, integer, and Boolean;
create, based on the profiling of the production data, a staging data for analytical purpose;
select at least one differential privacy technique from the plurality of differential privacy techniques based on one or more sensitive data fields of the staging data, the epsilon value, the sensitivity parameters of the data, and the privacy budget, wherein the plurality of differential privacy techniques comprises a Laplace classic, a Laplace bounded, an exponential and a random toss;
apply the selected at least one differential privacy technique iteratively on the staging data and an incremental addition of epsilon value at each iteration, wherein the incremental addition of epsilon at each iteration is based on a privacy loss at each iteration that is less than the privacy budget, wherein each of the selected at least one differential privacy technique generates a privacy enabled structured data, and wherein the epsilon value and the sensitivity parameters of the data are used as levers to control a degree of noise being added to the sensitive data fields by the selected at least one differential privacy technique; and
enable the user to select the differential privacy enabled structured data based on one or more results of the selected differential privacy technique application, wherein the one or more results include a set of privacy metrics providing information on privacy strength and similarity tolerance of each query in an interactive way.
6. The system of claim 5 , wherein one or more results comprise a histogram, a KDE Curve, a standard deviation, and a correlation for similarity strength.
7. The system of claim 5 , wherein the set of privacy metrics comprise of a privacy percentage, a privacy Digit match, and a privacy error.
8. The system of claim 7 , wherein the privacy percentage is calculated based on number of matches, a number of digits mismatch, and a distance difference.
9. A non-transitory computer readable medium storing one or more instructions which when executed by one or more processors on a system, cause the one or more processors to perform method comprising:
receiving, via an input/output interface, one or more sensitivity parameters of a structured data, an epsilon value, a plurality of differential privacy techniques, and a privacy budget selected by a user;
fetching, via one or more hardware processors, the structured data from a predefined database to generate a production data for a differential privacy, wherein the structured data is labelled and in a tabular form;
profiling, via the one or more hardware processors, the production data based on type and nature of the structured data, wherein the type of the structured data includes a numerical form, a categorical form, a binary form, and in a text form, and the nature of the structured data includes continuous, discrete, integer, and Boolean;
creating, via the one or more hardware processors, the data types-based on the profiling of the production data, a staging data for analytical purpose;
selecting, via the one or more hardware processors, at least one differential privacy technique from the plurality of differential privacy techniques based on one or more sensitive data fields of the staging data, the epsilon value, the sensitivity parameters of the data, and the privacy budget, wherein the plurality of differential privacy techniques comprises a Laplace classic, a Laplace bounded, an exponential and a random toss;
applying, via the one or more hardware processors, the selected at least one differential privacy technique iteratively on the staging data and an incremental addition of epsilon value at each iteration, wherein the incremental addition of epsilon at each iteration is based on a privacy loss at each iteration that is less than the privacy budget, wherein each of the selected at least one differential privacy technique generates a privacy enabled structured data, and wherein the epsilon value and the sensitivity parameters of the data are used as levers to control a degree of noise being added to the sensitive data fields by the selected at least one differential privacy technique; and
enabling, via the one or more hardware processors, the user to select the differential privacy enabled structured data based on one or more results of the selected differential privacy technique application, wherein the one or more results include a set of privacy metrics providing information on privacy strength and similarity tolerance of each query in an interactive way.