18403339. Pure Differentially Private Algorithms for Summation in the Shuffled Model simplified abstract (GOOGLE LLC)
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 18403339 titled 'Pure Differentially Private Algorithms for Summation in the Shuffled Model
The abstract describes an encoding method for privacy-preserving aggregation of private data, involving 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 a noise message based on a noise distribution for certain probabilistic statuses.
- The noise distribution discretizes a continuous unimodal distribution supported on a range of values.
- The multiset values are either based on the private value or represented by the noise message, depending on the probabilistic status.
- This method enables the aggregation of private data while preserving privacy through the use of noise messages.
Potential Applications: - Secure data aggregation in sensitive industries such as healthcare or finance. - Privacy-preserving statistical analysis in research settings. - Secure data sharing in collaborative projects.
Problems Solved: - Protecting the privacy of individual data while allowing for aggregate analysis. - Ensuring data security in scenarios where sensitive information needs to be shared. - Enabling collaborative data analysis without compromising individual privacy.
Benefits: - Enhanced data privacy protection. - Facilitates secure data sharing and collaboration. - Enables accurate aggregate analysis without revealing individual data.
Commercial Applications: Title: Secure Data Aggregation Method for Sensitive Industries This technology can be utilized in industries such as healthcare, finance, and research where secure data aggregation is crucial for analysis and decision-making. The market implications include improved data security, enhanced privacy compliance, and streamlined collaborative data projects.
Questions about Secure Data Aggregation Method: 1. How does the encoding method ensure privacy while aggregating private data? 2. What are the potential implications of using noise messages in data aggregation for privacy protection?
Frequently Updated Research: Stay updated on the latest advancements in secure data aggregation methods and privacy-preserving technologies to ensure compliance and data security in sensitive industries.
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.
