Collaboration between manufacturing partners always raises the concern that highly valuable Intellectual Property (IP) may not remain secure and leak, diminishing its value and potentially creating competitors. It’s for this reason that many potential partnerships are not considered feasible, eliminating many innovative and lucrative opportunities, and more broadly restricting industry growth.

Protecting sensitive manufacturing data from cyber threats, ensuring data privacy, and maintaining confidentiality create significant challenges in facilitating transparent data sharing.

Balancing the need for collaborative data sharing while safeguarding proprietary information poses a challenge, as manufacturers must navigate sharing insights without compromising competitive advantages or trade secrets.

Capital equipment vendors needing to improve their products can analyze product performance at customer sites without compromising highly proprietary customer use data. Using FHE to encrypt customer use data allows the vendor to achieve the two goals of guaranteeing customer use data remains completely confidential while still attaining the operational insights required to improve product efficiency and functionality.

FHE enables multiple cooperating parties, to contribute highly sensitive and valuable data, to partner in developing new business insights without the risk of exposing that data to potential exposure or breach. With FHE, collaboration is achieved securely, by allowing each partner to uniquely encrypt their data, having that data aggregated into a single data set, executing Machine Learning, Big Data, or Analytics on that data, and sharing the results within the partnership. In this way, partner-sensitive data remains confidential with the benefits of the collaboration being shared.

FHE-based data sharing opens up new areas of collaboration between partners. Without FHE the risk of data exposure and unauthorized access has been a significant barrier to collaboration. With FHE’s lattice-based, encryption those barriers are removed fostering greater collaboration, innovation, and value to industry participants.

FHE enables data privacy by maintaining encryption throughout derivative computation whether it be for Machine Learning, Big Data, or Analytics. Plaintext unencrypted data never appears throughout its computational lifecycle. This ensures the security and confidentiality of valuable and/or sensitive data but also allows compliance with stringent government regulations regarding the sharing of key manufacturing data considered to be national security assets.

FHE accelerates the velocity of innovation by guaranteeing the security and confidentiality of valuable and/or sensitive data while still enabling seamless collaboration among multiple stakeholders for analytics and insights that lead to better decision-making.