# US 2021/0279219 A1 · System and method for generating synthetic datasets > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279219 A1 - Document ID: US20210279219A1 - Status: Published application - Kind: A1 - Application number: 16/813,331 - Filed: 2020-03-09 - Published: 2021-09-09 - Earliest claimed: 2020-03-09 - Assignees: Truata Limited - Applicants: Truata Limited - Inventors: Michael Fenton; Imran Khan; Maurice Coyle - Web page: https://patentgrep.com/patent/US20210279219A1 ## CPC - G06F 16/2264: {Multidimensional index structures} - G06N 20/00: Machine learning - G06F 16/162: {Delete operations (erasing in storage systems G06F3/0652)} - G06F 16/221: {Column-oriented storage; Management thereof} - G06F 21/6245: {Protecting personal data, e.g. for financial or medical purposes} - G06F 21/6263: {during internet communication, e.g. revealing personal data from cookies} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system and method for generating one or more synthetic datasets with privacy and utility controls are disclosed. The system and method include an input/output (IO) interface for receiving at least one dataset and a set of privacy controls, at least one privacy controller that provides a set of fine-grained privacy and utility controls based on the received privacy controls for the at least one dataset, a data modeling engine to learn the analytical relationships of the received at least one dataset and to generate a risk and utility profile of the received at least one dataset, a data generation engine to apply learned models in accordance with the provided set of fine-grained privacy and utility controls from the privacy controller to produce one or more synthetic datasets, and a risk mitigation engine that iteratively targets configured risks within the one or more synthetic datasets and mitigates the targeted risks via modification of the one or more synthetic datasets, and outputs a risk profile for the one or more synthetic datasets. ## Claims ### Claim 1 A system for generating one or more synthetic datasets with privacy and utility controls, the system comprising: an input/output (IO) interface for receiving at least one dataset and a set of privacy controls to be applied to the at least one dataset; at least one privacy controller that receives the set of privacy controls and provides a set of fine-grained privacy and utility controls based on the received privacy controls for the at least one dataset; a data modeling engine to learn the analytical relationships of the received at least one dataset and to generate a risk and utility profile of the received at least one dataset; a data generation engine to apply learned models in accordance with the provided set of fine-grained privacy and utility controls from the privacy controller to produce one or more synthetic datasets; and a risk mitigation engine that iteratively targets configured risks within the one or more synthetic datasets and mitigates the targeted risks via modification of the one or more synthetic datasets, and outputs a risk profile for the one or more synthetic datasets, wherein the IO interface outputs the one or more synthetic datasets with known privacy and utility characteristics. ### Claim 2 (depends on 1) The system of claim 1 wherein the IO interface outputs the risk profile for the one or more synthetic datasets. ### Claim 3 (depends on 1) The system of claim 1 wherein the data modeling engine learns the analytical relationships of the received at least one dataset and generates a risk and utility profile of the received at least one dataset by extracting the relevant distributions from all columns in the dataset and calculating statistical relationships and correlations on the data. ### Claim 4 (depends on 1) The system of claim 1 wherein the data modeling engine outputs the extracted distributions to determine if correlations are permitted in the outputs the one or more synthetic datasets. ### Claim 5 (depends on 1) The system of claim 1 wherein a full correlation model is performed in the data modeling engine. ### Claim 6 (depends on 1) The system of claim 1 wherein a partial correlation model is performed in the data modeling engine. ### Claim 7 (depends on 1) The system of claim 1 wherein the data generation engine applies learned models in accordance with the provided set of fine-grained privacy and utility controls from the privacy controller to produce one or more synthetic datasets by checking the specification for the required output dataset, including number of rows, specific columns, and desired correlations. ### Claim 8 (depends on 1) The system of claim 1 wherein the data generation engine applies the permitted correlation models to generate correlated subsets of output data. ### Claim 9 (depends on 1) The system of claim 1 wherein the data generation engine applies the given distribution models to generate independent un-correlated subsets of output data. ### Claim 10 (depends on 1) The system of claim 1 wherein the risk mitigation engine finds hidden potential risks by searching through the original dataset to find potential hidden re-identification risks. ### Claim 11 (depends on 1) The system of claim 1 wherein the risk mitigation engine finds overt risks by searching through the generated dataset to find overt re-identification risks. ### Claim 12 (depends on 11) The system of claim 11 wherein the re-identification risks include potential risks specified in the privacy controls. ### Claim 13 (depends on 1) The system of claim 1 wherein the risk mitigation engine compares the original and generated datasets to identify hidden risks that may occur in the generated dataset. ### Claim 14 (depends on 1) The system of claim 1 wherein the risk mitigation engine applies mitigation techniques to the generated dataset based on the privacy controls. ### Claim 15 (depends on 14) The system of claim 14 wherein the mitigation techniques include at least one of deletion, multiplication, redaction, and fuzzing. ### Claim 16 (depends on 1) The system of claim 1 wherein the at least one privacy controller is configurable to set exact specification for privacy requirements for the dataset based on the privacy controls. ### Claim 17 (depends on 1) The system of claim 1 wherein the at least one privacy controller is configurable to set exact specification for analytical utility requirements for the dataset via utility controls. ### Claim 18 A method of generating synthetic datasets with privacy and utility controls, the method comprising: receiving, via an input/output (IO) interface, at least one dataset and a set of privacy controls to be applied to the at least one dataset; providing, via at least one privacy controller, a set of fine-grained privacy and utility controls based on the received privacy controls for the at least one dataset; establishing the analytical relationships of the received at least one dataset and generating a risk and utility profile of the received at least one dataset; applying learned models in accordance with the provided set of fine-grained privacy and utility controls from the privacy controller to produce one or more synthetic datasets; iteratively targeting configured risks within the one or more synthetic datasets and mitigating the targeted risks via modification of the one or more synthetic datasets; outputting the one or more synthetic datasets with known privacy and utility characteristics and a risk profile for the one or more synthetic datasets. ### Claim 19 (depends on 18) The method of claim 18, further comprising performing a threshold check on the output risk profile. ### Claim 20 (depends on 19) The method of claim 19, further comprising re-targeting configured risks if the threshold check are not under configured limits.