Current - Issue
Year 2026 · Volume 6 · Issue 5
Original Article
Temple Enclave AI: Tenant-Encrypted, Mutually Private LM Engine
Milind K. Patil1
1 Syncaissa Systems Inc, USA.
Published Online: September-October 2026
Pages: 46-58
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260605006
References
1. Ashkboos, Saleh, Amirkeivan Mohtashami, Maximilian L. Croci, et al. 2024. “QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.” Advances in Neural Information Processing Systems (NeurIPS).
2. Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. “Layer Normalization.” arXiv Preprint arXiv:1607.06450.
3. Beaver, Donald. 1991. “Efficient Multiparty Protocols Using Circuit Randomization.” Advances in Cryptology – CRYPTO ’91, 420–32.
4. Benaissa, Ayoub, Bilal Retiat, Bogdan Cebere, and Alaa Eddine Belfedhal. 2021. TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption. arXiv preprint arXiv:2104.03152.
5. Brakerski, Zvika. 2012. “Fully Homomorphic Encryption Without Modulus Switching from Classical GapSVP.” Advances in Cryptology – CRYPTO 2012, 868–86.
6. Chen, Tianyu, Hangbo Bao, Shaohan Huang, et al. 2022. “THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption.” Findings of ACL.
7. Costan, Victor, and Srinivas Devadas. 2016. “Intel SGX Explained.” IACR Cryptology ePrint Archive 2016 (86).
8. DeepSeek-AI. 2024. “DeepSeek-V3 Technical Report.” arXiv Preprint arXiv:2412.19437.
9. DeepSeek-AI. 2025. “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.” arXiv Preprint arXiv:2501.12948.
10. Fan, Junfeng, and Frederik Vercauteren. 2012. Somewhat Practical Fully Homomorphic Encryption. IACR Cryptology ePrint Archive, Report 2012/144.
11. Feyisetan, Oluwaseyi, Borja Balle, Thomas Drake, and Tom Diethe. 2020. “Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations.” Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM).
12. Hao, Meng, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang. 2022. “Iron: Private Inference on Transformers.” Advances in Neural Information Processing Systems (NeurIPS).
13. Knight, Kevin, Anish Nair, Nishit Rathod, and Kenji Yamada. 2006. “Unsupervised Analysis for Decipherment Problems.” Proceedings of COLING/ACL.
14. Li, Dacheng, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, and Hao Zhang. 2023. “MPCFormer: Fast, Performant and Private Transformer Inference with MPC.” International Conference on Learning Representations (ICLR).
15. Liu, Zechun, Changsheng Zhao, Igor Fedorov, et al. 2024. “SpinQuant: LLM Quantization with Learned Rotations.” arXiv Preprint arXiv:2405.16406.
16. Lu, Wen-jie, Zhicong Huang, Zhen Gu, et al. 2025. “BumbleBee: Secure Two-Party Inference Framework for Large Transformers.” Network and Distributed System Security Symposium (NDSS).
17. Microsoft Research. 2023. Microsoft SEAL (Release 4.1). Https://github.com/Microsoft/SEAL.
18. Mishra, Pratyush, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. 2020. “Delphi: A Cryptographic Inference Service for Neural Networks.” USENIX Security Symposium.
19. Morris, John X., Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander M. Rush. 2023. “Text Embeddings Reveal (Almost) as Much as Text.” Proceedings of EMNLP.
20. Pang, Qi, Jinhao Zhu, Helen Möllering, Wenting Zheng, and Thomas Schneider. 2024. “BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers.” IEEE Symposium on Security and Privacy (s&p).
21. Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. “Language Models Are Unsupervised Multitask Learners.” OpenAI Technical Report.
22. Schönemann, Peter H. 1966. “A Generalized Solution of the Orthogonal Procrustes Problem.” Psychometrika 31 (1): 1–10.
23. Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. 2017. “Attention Is All You Need.” Advances in Neural Information Processing Systems (NeurIPS).
24. Vepakomma, Praneeth, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018. “Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data.” arXiv Preprint arXiv:1812.00564.
25. Yuan, Youliang, Wenxiang Jiao, Wenxuan Wang, et al. 2024. “GPT-4 Is Too Smart to Be Safe: Stealthy Chat with LLMs via Cipher.” International Conference on Learning Representations (ICLR).
26. Zhang, Biao, and Rico Sennrich. 2019. “Root Mean Square Layer Normalization.” Advances in Neural Information Processing Systems (NeurIPS).
27. Zhou, Xin, Yi Lu, Ruotian Ma, et al. 2023. “TextObfuscator: Making Pre-Trained Language Model a Privacy Protector via Obfuscating Word Representations.” Findings of ACL.
2. Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. “Layer Normalization.” arXiv Preprint arXiv:1607.06450.
3. Beaver, Donald. 1991. “Efficient Multiparty Protocols Using Circuit Randomization.” Advances in Cryptology – CRYPTO ’91, 420–32.
4. Benaissa, Ayoub, Bilal Retiat, Bogdan Cebere, and Alaa Eddine Belfedhal. 2021. TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption. arXiv preprint arXiv:2104.03152.
5. Brakerski, Zvika. 2012. “Fully Homomorphic Encryption Without Modulus Switching from Classical GapSVP.” Advances in Cryptology – CRYPTO 2012, 868–86.
6. Chen, Tianyu, Hangbo Bao, Shaohan Huang, et al. 2022. “THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption.” Findings of ACL.
7. Costan, Victor, and Srinivas Devadas. 2016. “Intel SGX Explained.” IACR Cryptology ePrint Archive 2016 (86).
8. DeepSeek-AI. 2024. “DeepSeek-V3 Technical Report.” arXiv Preprint arXiv:2412.19437.
9. DeepSeek-AI. 2025. “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.” arXiv Preprint arXiv:2501.12948.
10. Fan, Junfeng, and Frederik Vercauteren. 2012. Somewhat Practical Fully Homomorphic Encryption. IACR Cryptology ePrint Archive, Report 2012/144.
11. Feyisetan, Oluwaseyi, Borja Balle, Thomas Drake, and Tom Diethe. 2020. “Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations.” Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM).
12. Hao, Meng, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang. 2022. “Iron: Private Inference on Transformers.” Advances in Neural Information Processing Systems (NeurIPS).
13. Knight, Kevin, Anish Nair, Nishit Rathod, and Kenji Yamada. 2006. “Unsupervised Analysis for Decipherment Problems.” Proceedings of COLING/ACL.
14. Li, Dacheng, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, and Hao Zhang. 2023. “MPCFormer: Fast, Performant and Private Transformer Inference with MPC.” International Conference on Learning Representations (ICLR).
15. Liu, Zechun, Changsheng Zhao, Igor Fedorov, et al. 2024. “SpinQuant: LLM Quantization with Learned Rotations.” arXiv Preprint arXiv:2405.16406.
16. Lu, Wen-jie, Zhicong Huang, Zhen Gu, et al. 2025. “BumbleBee: Secure Two-Party Inference Framework for Large Transformers.” Network and Distributed System Security Symposium (NDSS).
17. Microsoft Research. 2023. Microsoft SEAL (Release 4.1). Https://github.com/Microsoft/SEAL.
18. Mishra, Pratyush, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. 2020. “Delphi: A Cryptographic Inference Service for Neural Networks.” USENIX Security Symposium.
19. Morris, John X., Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander M. Rush. 2023. “Text Embeddings Reveal (Almost) as Much as Text.” Proceedings of EMNLP.
20. Pang, Qi, Jinhao Zhu, Helen Möllering, Wenting Zheng, and Thomas Schneider. 2024. “BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers.” IEEE Symposium on Security and Privacy (s&p).
21. Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. “Language Models Are Unsupervised Multitask Learners.” OpenAI Technical Report.
22. Schönemann, Peter H. 1966. “A Generalized Solution of the Orthogonal Procrustes Problem.” Psychometrika 31 (1): 1–10.
23. Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. 2017. “Attention Is All You Need.” Advances in Neural Information Processing Systems (NeurIPS).
24. Vepakomma, Praneeth, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018. “Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data.” arXiv Preprint arXiv:1812.00564.
25. Yuan, Youliang, Wenxiang Jiao, Wenxuan Wang, et al. 2024. “GPT-4 Is Too Smart to Be Safe: Stealthy Chat with LLMs via Cipher.” International Conference on Learning Representations (ICLR).
26. Zhang, Biao, and Rico Sennrich. 2019. “Root Mean Square Layer Normalization.” Advances in Neural Information Processing Systems (NeurIPS).
27. Zhou, Xin, Yi Lu, Ruotian Ma, et al. 2023. “TextObfuscator: Making Pre-Trained Language Model a Privacy Protector via Obfuscating Word Representations.” Findings of ACL.
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