Method for Amplifying and Commoditizing Latent Value in Network-Accessible Data Stores

Unmet Need
The age of technology has allowed data to be stored on devices, servers, or in the cloud. Such a store of data typically arises through the engagement of an individual with one of many kinds of ‘Service Providers’ (SPs) that record data about the individual on a database that the SP operates. This data is exclusive only to the SP and inaccessible to outsiders. However, if other SPs were able to access each other’s data, this would benefit both the SPs and the individuals associated with elementary pieces of data. Sharing or collaborating with other SPs would be mutually beneficial for both the SPs and the individuals; it would result in increases in the value of ‘statistical insight’ while simultaneously maintaining the individuals’ anonymity. There is a need to connect SPs and provide a service for data collaboration that can benefit one or more of the participating providers and individuals associated with the elementary pieces of data used.

Technology Overview
Researchers from Johns Hopkins University Institute for Data Intensive Engineering and Sciences have developed a method for amplifying and commoditizing latent value in network-accessible data stores. The method allows SPs to synthesize elementary data from end users into usable statistical products that can be interpreted quickly by most consumers, thus unlocking the potential for unused data stores to be converted into value-generating statistical insights. Access to these statistical products yields strategic information for other SPs, who in exchange provide access to their own statistical products, thus creating a shared data ecosystem where SPs mutually benefit by the exchange of data. In order to compensate their users for supplying data, the SPs may sell access to the statistical products to generate revenue and distribute the gains to the users. The security of the data and anonymity of the users who supply the elementary data is maintained through statistical-disclosure control. Unlike SPs’ existing data collection framework, which requires each SP to independently gather elementary level data from their consumers and process it for higher-level insights, the proposed method will allow for efficient high-level data collection from a wide variety of data sources, thus reducing the data collection burden on SPs.

Stage of Development
The system and methods have been fully developed and a web application embodying the disclosed method has been proposed.

 

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