Mobito Marketplace feature release: Dynamic Data Masking

Mobito Marketplace Data Exchange Tools

At Mobito we strive to unlock the value of data for companies and society at large. Exchanging data should not be limited by regulations that attempt to correct it but rather evolve to reflect the multi-stakeholders requirements. Acknowledging the uncertainty, anxiety and lack of comfort that organisations feel in navigating these requirements, we are developing tools that empower data owners with data sharing control. With dynamic data masking, data owners can select what part of their data to share with which recipients, allowing more precision and flexibility in data sharing

Data-privacy VS data exchange incentives

First some context. 3 years have passed since GDPR (General Data Protection Regulation) was established as a new law governing data privacy across EU countries. Worldwide, the discussion for data privacy protection has been evolving and has led to other regulations including the Australia’s Privacy Act, Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) and the most recent California Consumer Privacy Act (CCPA). At the same time, the appetite of companies and industries to combine data from different sources for the generation of smarter products and services is increasing. This appetite is often at odds with data sharing restrictions that may paralyze initiatives that lack the knowledge to achieve business goals while preserving privacy and respecting relevant regulations.

Similar restrictions have existed long before GDPR as businesses, governments and organizations needed to communicate data with third parties and internal collaborators while taking care of data-parts that were private or confidential from a personal, enterprise and commercial standpoint. As more data is becoming available from IOT devices and enterprise operations, data sharing is increasingly locking horns with data-privacy concerns.

Use Cases

The mobility space and the movement and interaction of people generate massive amounts of data that can fuel innovation and better services that address a lot of the urban-life challenges. However, mobility data is often classified as personal data, insofar the geolocation of individuals can be revealing of an individual’s identity when combined with other datasets (e.g. addresses). Therefore it becomes a challenge for data owners including cities and mobility operators to utilize effectively this valuable informational asset while remaining compliant.