18212438. METHOD AND SYSTEM FOR ENSURING PRIVACY PROTECTION FOR DATASETS USING SPACE PARTITIONING TECHNIQUES simplified abstract (JPMorgan Chase Bank, N.A.)
Contents
METHOD AND SYSTEM FOR ENSURING PRIVACY PROTECTION FOR DATASETS USING SPACE PARTITIONING TECHNIQUES
Organization Name
Inventor(s)
Eleonora Kreacic of London (GB)
Vamsi Krishna Potluru of New York NY (US)
Tucker Richard Balch of Suwanee GA (US)
Manuela Veloso of New York NY (US)
METHOD AND SYSTEM FOR ENSURING PRIVACY PROTECTION FOR DATASETS USING SPACE PARTITIONING TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18212438 titled 'METHOD AND SYSTEM FOR ENSURING PRIVACY PROTECTION FOR DATASETS USING SPACE PARTITIONING TECHNIQUES
Simplified Explanation: The patent application discusses systems and methods for generating a synthetic dataset that maintains the original data while ensuring privacy through differential privacy techniques.
Key Features and Innovation:
- Data-independent and data-dependent algorithms for differentially private dataset release.
- Algorithms based on space partitioning for preserving privacy while answering statistical queries.
- Intuitive and transparent algorithms with theoretical results on utility-privacy tradeoffs.
- Data-dependent approach overcomes the curse of dimensionality, leading to a scalable algorithm.
Potential Applications: This technology can be applied in industries such as healthcare, finance, and research where privacy-preserving data analysis is crucial.
Problems Solved: The technology addresses the challenge of balancing data utility and privacy in the release of synthetic datasets.
Benefits:
- Ensures privacy protection while maintaining the utility of the data.
- Provides a scalable solution for generating synthetic datasets.
- Offers transparency and theoretical guarantees on privacy preservation.
Commercial Applications: The technology can be utilized in data analytics companies, research institutions, and government agencies for secure data sharing and analysis.
Prior Art: Researchers can explore prior work on differential privacy, synthetic data generation, and privacy-preserving algorithms in related fields.
Frequently Updated Research: Stay updated on advancements in differential privacy techniques, synthetic data generation, and privacy-preserving algorithms for data analysis.
Questions about Differential Privacy: 1. What are the key challenges in implementing differential privacy in real-world applications? 2. How can differential privacy techniques be optimized for specific industries or use cases?
Original Abstract Submitted
Systems and methods for generation of a synthetic dataset that simultaneously represents the original data and preserves privacy are provided. The objective of answering statistical queries in a differentially-private manner is addressed by providing data-independent and data-dependent algorithms based on space partitioning for differentially private dataset release. These algorithms are intuitive and transparent, resulting in theoretical results on the utility-privacy tradeoffs where utility is measured with respect to kernel density preservation. The data-dependent approach overcomes the curse of dimensionality and leads to a scalable algorithm.
