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secureTF: A Secure TensorFlow Framework

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LEGATO project
LEGATO project

LEGaTO paper presented at Middleware 2020 by Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkeland, Pramod Bhatotia and Christof Fetzer

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1secureTF: A Secure TensorFlow Framework – Middleware 2020 1 secureTF: A Secure TensorFlow Framework Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkel, Pramod Bhatotia, Christof Fetzer
2secureTF: A Secure TensorFlow Framework – Middleware 2020 2 Cloud Provider Motivation Data Clients Training Data Training Model Inference How to ensure confidentiality and integrity of data, code (e.g., Python code), model and computation with low performance overhead while retaining accuracy?
3secureTF: A Secure TensorFlow Framework – Middleware 2020 3 Application Application Libraries (Enclave) Hypervisor Container Engine Operating System Host SGX (Software Guard eXtensions) is a set of processor extensions for establishing a TEE inside an application Intel SGX Intel SGX protects the integrity and confidentiality of applications
4secureTF: A Secure TensorFlow Framework – Middleware 2020 4 Several works rely on Intel SGX to support secure machine learning: • Privado [Microsoft Research 2019] • Slalom [ICLR2019] • Occlumency [MobiCom19] • … State-of-the-art systems Limitations: • Focuses only for secure inferences, not for training computation • Does not support distributed setting • Supports only a limited number of operators
5secureTF: A Secure TensorFlow Framework – Middleware 2020 5 Cloud Provider User secureTF: Overview secureTF (Enclave) SGXTLS (1) Remote Attestation (2) Keys, certificate transferring (3) Computation results Data & Code Model
6secureTF: A Secure TensorFlow Framework – Middleware 2020 6 Clients Cloud Provider Data User TLS Attestation & policy submitting TLS TLSTLS Attestation & secrets provision Training Data Inference Training Model SGX SGX 1. Protect against attackers with privileged/root accesses Configuration & Attestation Service (CAS) SGX 2. Perform transparently attestation & Key Management secureTF: Design
7secureTF: A Secure TensorFlow Framework – Middleware 2020 7 TensorFlow Application (e.g., Python code) secureTF TensorFlow Libraries (Enclave) Network Shield File system shield M:N Threading secureTF Controller SCONE Runtime Intel SGX Driver Container (cgroups) Host Operating System Untrusted Reduce #Enclave entries/exits Actively protect data, model, and code secureTF: Implementation
8secureTF: A Secure TensorFlow Framework – Middleware 2020 8 secureTF: Evaluation 0 500 1000 1500 2000 2500 Native musl Native glibc secureTF SIM secureTF HW Graphene SGX Latency(milliseconds) Inception_v4 (163MB) Inception_v3 (91MB) Lower the better secureTF incurs ~5% in SIM mode, ~22% overhead in HW mode compared to native versions secureTF is ~1.1X – 1.4X faster than Graphene-SGX based system
9secureTF: A Secure TensorFlow Framework – Middleware 2020 9 secureTF: A Secure TensorFlow Framework • Transparency: supports unmodified TensorFlow applications (both training and inferences) • Security: provides end-to-end security for the input data, ML model, and application code • Accuracy: maintains the same in the native TensorFlow framework Conclusion Thank you!
10secureTF: A Secure TensorFlow Framework – Middleware 2020 10 Products https://sconedocs.github.io https://scontain.com Contact do@scontain.com

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secureTF: A Secure TensorFlow Framework

  • 1. 1secureTF: A Secure TensorFlow Framework – Middleware 2020 1 secureTF: A Secure TensorFlow Framework Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkel, Pramod Bhatotia, Christof Fetzer
  • 2. 2secureTF: A Secure TensorFlow Framework – Middleware 2020 2 Cloud Provider Motivation Data Clients Training Data Training Model Inference How to ensure confidentiality and integrity of data, code (e.g., Python code), model and computation with low performance overhead while retaining accuracy?
  • 3. 3secureTF: A Secure TensorFlow Framework – Middleware 2020 3 Application Application Libraries (Enclave) Hypervisor Container Engine Operating System Host SGX (Software Guard eXtensions) is a set of processor extensions for establishing a TEE inside an application Intel SGX Intel SGX protects the integrity and confidentiality of applications
  • 4. 4secureTF: A Secure TensorFlow Framework – Middleware 2020 4 Several works rely on Intel SGX to support secure machine learning: • Privado [Microsoft Research 2019] • Slalom [ICLR2019] • Occlumency [MobiCom19] • … State-of-the-art systems Limitations: • Focuses only for secure inferences, not for training computation • Does not support distributed setting • Supports only a limited number of operators
  • 5. 5secureTF: A Secure TensorFlow Framework – Middleware 2020 5 Cloud Provider User secureTF: Overview secureTF (Enclave) SGXTLS (1) Remote Attestation (2) Keys, certificate transferring (3) Computation results Data & Code Model
  • 6. 6secureTF: A Secure TensorFlow Framework – Middleware 2020 6 Clients Cloud Provider Data User TLS Attestation & policy submitting TLS TLSTLS Attestation & secrets provision Training Data Inference Training Model SGX SGX 1. Protect against attackers with privileged/root accesses Configuration & Attestation Service (CAS) SGX 2. Perform transparently attestation & Key Management secureTF: Design
  • 7. 7secureTF: A Secure TensorFlow Framework – Middleware 2020 7 TensorFlow Application (e.g., Python code) secureTF TensorFlow Libraries (Enclave) Network Shield File system shield M:N Threading secureTF Controller SCONE Runtime Intel SGX Driver Container (cgroups) Host Operating System Untrusted Reduce #Enclave entries/exits Actively protect data, model, and code secureTF: Implementation
  • 8. 8secureTF: A Secure TensorFlow Framework – Middleware 2020 8 secureTF: Evaluation 0 500 1000 1500 2000 2500 Native musl Native glibc secureTF SIM secureTF HW Graphene SGX Latency(milliseconds) Inception_v4 (163MB) Inception_v3 (91MB) Lower the better secureTF incurs ~5% in SIM mode, ~22% overhead in HW mode compared to native versions secureTF is ~1.1X – 1.4X faster than Graphene-SGX based system
  • 9. 9secureTF: A Secure TensorFlow Framework – Middleware 2020 9 secureTF: A Secure TensorFlow Framework • Transparency: supports unmodified TensorFlow applications (both training and inferences) • Security: provides end-to-end security for the input data, ML model, and application code • Accuracy: maintains the same in the native TensorFlow framework Conclusion Thank you!
  • 10. 10secureTF: A Secure TensorFlow Framework – Middleware 2020 10 Products https://sconedocs.github.io https://scontain.com Contact do@scontain.com