Unverified50% confidenceFactExact time
FrontierMath adopts confidentiality measures such as keeping problems undisclosed and granting access only to model submitters in order to reduce the risk of data contamination.
1
Sources
50%
Confidence
Long-term
Relevance
10/2/2026
First Seen
Sources
Related Entities
Related Claims
Unverified机密计算使云平台管理员也无法窥探受保护环境中的数据，将零信任边界延伸至计算层67% similarUnverifiedIf OpenAI's privacy-safe processing were implemented based on TEE, it might require using a smaller dedicated security classification model rather than a full large language model to perform risk assessment.67% similarVerifiedPrivacy-preserving technologies such as federated learning and on-device inference allow models to process data on the user's device, avoiding the upload of raw personal data to the cloud.66% similarVerifiedOnce open-source model weights are made public, attackers can remove a model's safety restrictions through fine-tuning.64% similarUnverified联邦学习通过仅上传加密的模型梯度更新而非原始数据来缓解隐私担忧，但面临通信开销大、设备算力异构、数据非独立同分布等挑战63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/968627API
curl https://kongchang.com/api/v1/knowledge/claims/968627MCP
get_claim(id=968627)