Fair Math

Fair Math is a United States-based technology company that develops cryptographic infrastructure for computing on encrypted data. According to its official website, the company positions itself around "Programmable Confidentiality" powered by a new generation of fully homomorphic encryption (FHE). Its stated mission is to remove the fundamental privacy barrier that keeps sensitive data out of AI and modern compute infrastructure in critical sectors. The company's offering centers on two broad capability areas. The first is private AI computation: enabling AI models to process encrypted data directly, so that secure training and inference can occur without exposing sensitive information. The second is confidential data infrastructure: cryptographic infrastructure that lets organizations run analytics, services, and cross-organization computation over sensitive data in untrusted environments without ever decrypting it. Fair Math describes use cases including encrypted data pipelines, confidential cloud workloads, and cross-organization computation. Fair Math claims its approach delivers "1000x faster evaluation amortized time vs 3rd & 4th generation FHE," supports arbitrary functions on encrypted data, and uses SIMD vectorized processing to handle thousands of values simultaneously. These are vendor-stated performance claims rather than independently verified figures. The company is registered in Dover, United States, according to Wikidata. It maintains a public presence through its website and engages with the cryptography and AI research communities; its site notes that its team plans to attend major cryptography and AI conferences worldwide in 2026–2027, including ACM CCS 2026 and WAHC 2026 in The Hague, Netherlands. Fair Math presents itself as a research-driven infrastructure provider rather than an end-user application vendor, targeting organizations in critical sectors that handle sensitive data.

Fair Math is a United States-based company registered in Dover, according to Wikidata. Public information about its founding date, founders, and funding history is limited. The company has built its public identity around the development of cryptographic infrastructure for encrypted computation, specifically leveraging a new generation of fully homomorphic encryption (FHE). On its official website, Fair Math describes its mission as removing "the fundamental privacy barrier that keeps sensitive data out of AI and modern compute infrastructure in critical sectors." The company frames its work as enabling two main categories of use: private AI computation, where AI models process encrypted data directly for secure training and inference, and confidential data infrastructure, where organizations run analytics, services, and cross-organization computation over sensitive data in untrusted environments without decryption. Fair Math claims significant performance improvements over earlier FHE generations, stating that its approach delivers "1000x faster evaluation amortized time vs 3rd & 4th generation FHE," supports arbitrary function evaluation on encrypted data, and uses SIMD vectorized processing to handle thousands of values simultaneously. These claims are presented on the company's own website and have not been independently verified in the available evidence. The company maintains an active presence in the cryptography and AI research communities. Its website notes that its team will attend major conferences worldwide in 2026–2027, including ACM CCS 2026 and WAHC 2026 in The Hague, Netherlands. This conference engagement suggests an ongoing commitment to research and development in encrypted computing. As of the available evidence, Fair Math operates as a private company with no disclosed parent organization or ownership structure.

• Fair Math team plans to attend ACM CCS 2026 in The Hague, Netherlands.

• Fair Math team plans to attend WAHC 2026 in The Hague, Netherlands.

Research-driven cryptographic infrastructure provider enabling computation on encrypted data for AI and sensitive workloads in critical sectors.

• Programmable Confidentiality platform

  Cryptographic infrastructure for computing on encrypted data, powered by new generation FHE.

• Private AI computation

  Enables AI models to process encrypted data directly for secure training and inference without exposing sensitive information.

• Confidential data infrastructure

  Lets organizations run analytics, services, and cross-organization computation over sensitive data in untrusted environments without decryption.

1. 1Fair Math - Programmable Confidentialityfairmath.xyz
2. 2Fair Math (United States) - Wikidatawikidata.org