Third-party data access is a growing requirement for businesses. Companies need to provide access to data for external parties for a variety of reasons, from R&D projects to enabling business partners. The realities of providing access, though, are complex. Given the heightened risks, standards for security and privacy are even greater when a third-party is involved. Multiple breaches have demonstrated that traditional approaches, such as aggregating, masking or redacting sensitive data, do not provide any meaningful privacy protection.
Third-party data access is one of the primary use cases of LeapYear. LeapYear fundamentally changes the data access paradigm, giving data owners complete control of their data and ensuring proven privacy, while still enabling third-party data access and self-service analytics.
- Retain control of your data – With LeapYear, data stays with your organization. Third-parties can generate analytics, representative datasets and models from the data, but cannot see or extract the data itself.
- Mathematically proven privacy – LeapYear embeds differential privacy into every computation run by the third-party. This protects data not only against viewing or extraction but also against sophisticated privacy attacks. Unlike anonymization or data masking, LeapYear provides mathematically proven privacy.
- Simplify and accelerate data sharing – Companies trust LeapYear to eliminate costly workflows for third-party data access. The technology works seamlessly across all data types and at scale, without needing to know which information is sensitive. The privacy assurances hold even as your data grows and changes.
The OutCome - A Major Technology Company
A major technology company needed to provide third-party access to its user data for research and partnerships. Traditional approaches that required anonymizing or aggregating the data were insufficient for several reasons. These methods did not protect user privacy; the company did not want to take the risk of anyone reverse engineering user information. These approaches were also costly in terms of data value; they reduced the information content of the data, reducing model quality and even created spurious signals. Finally, the data was affected by multiple privacy regulations from several countries with strict privacy laws requiring time consuming and costly compliance reviews in the event the data needed to be updated.
With LeapYear, the technology company was able to avoid inefficiency, better protect its user’s data, and drastically improve value for its partners. LeapYear’s system runs at petabyte scale, protecting data on hundreds of millions of users, enabling the technology company to share data with over 50 partners.