The Perfect Mediator for Secured Data Sharing

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5 Min Read

The Perfect Mediator for Secured Data Sharing

With data flood on the rise, the need to securely access/share data has become a major challenge like never before. Oftentimes, IT teams spend more time in understanding requests to provide data and end users suffer because of longer waiting periods to obtain the data after they have put in a request. Meanwhile, when copies of data are stored at various positions, hackers can easily find the weakest point of entry. As a provider of a privacy protected, data sharing platform, Bitnobi allows data providers to set the rules of engagement for end-user data queries. End users can prototype, review, and build their data query all through a web-based version of Bitnobi that has all of the standard data transformation tools needed to build a data query. “Our software acts as an interface between a data provider and end users in order to provide a more efficient way of interacting with data sources without giving raw data or making copies of them,” says Hassan Jaferi, CEO of Bitnobi Inc. The company is a York University startup supported by MaRS Innovation and Innovation York and their core technology is created by big data, cloud computing, and cybersecurity experts who identified the problem of data sharing while working in industry.

Because their software allows end users to interact with data sources without giving raw data or making copies of data, Bitnobi is an important player in the advancement of personalized healthcare. In fact, their technology can be used in a variety of personalized healthcare initiatives, including Canadian Personalized Health Innovation Network (CPHIN) and the Terry Fox Research Institute’s (TFRI) Digital Health and Discovery Platform (DHDP). Today, Bitnobi works with CPHIN and applies its software within CPHIN’s Kick Start Programs that include elements of access to de-identified data through a query platform. These programs generate evidence necessary to support system transformation in support of an ecosystem wherein de-identified data is findable, accessible, interoperable, and resusable. By leveraging a number of different and proprietary healthcare data sources, Kick Start Programs demonstrate the value of insights coming from big data in health. Because the data sources are controlled by their respective data owners (stewards) and use different types of data architecture, this is precisely the scenario that Bitnobi can help with as it provides the data owners with the ability of setting rules of engagement on who can see certain types of data.

With Bitnobi’s software, queries are run through a federated system that can run a portion of each query on the respective data source and return a final aggregate data segment back to the end user. This is particularly important when a researcher needs to answer a health question that requires genomic data, electronic medical record data and laboratory data. In many healthcare settings, these three different data repositories reside within different databases that do not necessarily communicate with each other. The time it takes to secure access to such data can take a lot of time due to privacy concerns. With Bitnobi, the owners of these data repositories can set their rules of engagement based on data governance rules and then allow researchers to query based on a preview of the data.

Going forward, the main focus for Bitnobi is to get their software ready for security and penetration testing which will help facilitate any security certification process needed to convince future customers in using the software. According to Jaferi, “There are a number of activities in the technology map specifically leveraging blockchain to secure the data audit log for the data provider is a key feature that we plan on introducing.”


Company: Bitnobi


Management:  Hassan Jaferi, CEO

Founded Year: 2016

Headquarters:  Toronto, Ontario

Description: A provider of a privacy-protected and data sharing platform that allows data providers to set the rules of engagement for end-user data queries

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