How does YData guarantee the integrity of data generated synthetically?

YData implements an automated quality and privacy control procedure for each generated dataset in order to regulate the utility, quality, and privacy of the newly produced data. Divergence metrics, correlation measures, and non-parametric tests are employed to assess quality, while the TSTR (Train Synthetic Test Real) methodology is utilized to evaluate utility. When it comes to assessing privacy infringement, we conduct a range of experiments, including inference assaults.

How does YData guarantee that synthetic data adheres to privacy regulations?

Regarding privacy and security, YData's solution is substantiated by a substantial body of literature. At present, neither a privacy certification nor a recognized process exists to certify a solution's compliance with privacy regulations. However, synthetic data satisfies the requirements of the GDPR and other privacy regulations due to its generation using random noise as input. It is unfeasible to trace a synthetic record back to an original data record, which is analogous to the definition of GDPR.

How is it guaranteed that the data never departs your infrastructure by YData?

By deploying its platform on your organization's infrastructure (whether in the cloud or on-premises), YData guarantees the absence of any data transmission between your organization and YData.

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YData

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YData Fabric enables users to create high-quality datasets, generate synthetic data, and manage data administration efficiently, enhancing AI model development and productivity.

Key AI Functions:IT,hr,human resources,information technology,github,open sourc,productivity tools

Description for YData

YData Fabric is a data-centric AI tool that allows users to create high-quality datasets, generate synthetic data, and manage data administration.

Features of YData

Synthetic Data Generation: Constructs synthetic datasets mimicking authentic data behavior, enhancing model efficiency and safeguarding sensitive information.
Data Catalog: Profiles and administers data, offering an interactive catalog for evaluating and monitoring data deviations and changes.
Data-Centric Approach: Facilitates AI development by enhancing the machine learning development flow.
Open-Source Tools: Provides tools like ydata-synthetic and ydata-profiling for generating synthetic tabular and time-series data, and facilitating exploratory data analysis (EDA).
Community Support: Offers access to a community of thought leaders and data-centric AI specialists.
Productivity Boost: Increases data scientists' productivity by a hundredfold.
Efficiency Improvement: Facilitates a 25% reduction in AI model delivery time and up to 50% reduction in time-to-market.
Model Efficacy: Achieves a 20% increase in model efficacy through enhanced data quality.

Use Cases for YData

Financial Services: YData can assist the payment sector in increasing its efficiency.

The platform is capable of managing data associated with telecommunications.

YData is capable of managing information pertaining to utilities, including electricity, water supplies, and public services.

The retail platform can safeguard data for renowned brands.

Healthcare & Pharma: YData can assist with data discovery in the healthcare industry.

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