Google llc (20240236052). Pure Differentially Private Algorithms for Summation in the Shuffled Model simplified abstract
Contents
Pure Differentially Private Algorithms for Summation in the Shuffled Model
Organization Name
Inventor(s)
Badih Ghazi of San Jose CA (US)
Noah Zeger Golowich of Lexington MA (US)
Shanmugasundaram Ravikumar of Piedmont CA (US)
Pasin Manurangsi of Mountain View CA (US)
Ameya Avinash Velingker of San Francisco CA (US)
Rasmus Pagh of Berkeley CA (US)
Pure Differentially Private Algorithms for Summation in the Shuffled Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240236052 titled 'Pure Differentially Private Algorithms for Summation in the Shuffled Model
The abstract describes an encoding method for privacy-preserving aggregation of private data. It involves obtaining private data with a private value, determining a probabilistic status, producing a multiset with multiple values, and providing the multiset for aggregation with additional multisets generated for other private values.
- The method involves producing multiset values based on the private value or a noise message, depending on the probabilistic status.
- The noise message is generated using a noise distribution that discretizes a continuous unimodal distribution supported on a range of values.
Potential Applications: - Data aggregation in sensitive industries such as healthcare or finance. - Statistical analysis while preserving individual privacy. - Secure data sharing in research collaborations.
Problems Solved: - Protecting the privacy of individual data while allowing for aggregation. - Ensuring data security and confidentiality in statistical analysis. - Enabling collaborative research without compromising sensitive information.
Benefits: - Enhanced privacy protection for individuals. - Secure and accurate data aggregation for analysis. - Facilitates collaboration and data sharing in a secure manner.
Commercial Applications: Title: Secure Data Aggregation Method for Sensitive Industries This technology could be utilized in healthcare analytics, financial data analysis, market research, and collaborative scientific studies. It offers a secure way to aggregate and analyze private data while maintaining confidentiality and privacy.
Prior Art: Readers can explore prior research on secure data aggregation methods, privacy-preserving algorithms, and statistical analysis techniques in sensitive industries to understand the evolution of this technology.
Frequently Updated Research: Researchers are continually exploring new methods to enhance privacy-preserving data aggregation techniques, improve noise distribution algorithms, and optimize secure data sharing protocols in various industries.
Questions about Privacy-Preserving Data Aggregation: 1. How does this method ensure the privacy of individual data during aggregation? - The method uses noise messages and probabilistic conditions to protect private values while allowing for statistical analysis.
2. What are the potential implications of this technology in industries like healthcare and finance? - This technology could revolutionize data analysis in sensitive industries by enabling secure aggregation and analysis of private data.
Original Abstract Submitted
an encoding method for enabling privacy-preserving aggregation of private data can include obtaining private data including a private value, determining a probabilistic status defining one of a first condition and a second condition, producing a multiset including a plurality of multiset values, and providing the multiset for aggregation with a plurality of additional multisets respectively generated for a plurality of additional private values. in response to the probabilistic status having the first condition, the plurality of multiset values is based at least in part on the private value, and in response to the probabilistic status having the second condition, the plurality of multiset values is a noise message. the noise message is produced based at least in part on a noise distribution that comprises a discretization of a continuous unimodal distribution supported on a range from zero to a number of multiset values included in the plurality of multiset values.
