Practical Deep Neural Network Protection for Unmodified Applications in Intel Software Guard Extension Environments

Kang, D. M., Faahym, H., Meftah, S., Keoh, S. L. and Khin, M. M. A. (2023) Practical Deep Neural Network Protection for Unmodified Applications in Intel Software Guard Extension Environments. In: Staggs, J. and Shenoi, S. (eds.) Critical Infrastructure Protection XVII. ICCIP 2023. IFIP Advances in Information and Communication Technology. Springer: Cham, pp. 177-192. ISBN 9783031495847 (doi: 10.1007/978-3-031-49585-4_9)

Abstract

Trusted computing, often referred to as confidential computing, is an attempt to enhance the trust of modern computer systems through a combination of software and hardware mechanisms. The area increased in popularity after the release of the Intel Software Guard Extensions software development kit, enabling industry actors to create applications compatible with the interfaces required to leverage secure enclaves. However, the prime choices of users are still libraries and solutions that facilitate code portability to Software Guard Extension environments without any modifications to native applications. While these have proved effective at eliminating additional development costs, they inherit all the security concerns for which Software Guard Extensions has been criticized. This chapter proposes a split computing method to enhance the privacy of deep neural network models outsourced to trusted execution environments. The key metric that guides the approach is split computing performance that does not involve architectural modifications to deep neural network models. The model partitioning method enables stricter security guarantees while producing negligible levels of overhead. This chapter also discusses the challenges involved in developing a pragmatic solution against established Intel Software Guard Extensions attacks. The results demonstrate that the method introduces negligible performance overhead and reliably secures the outsourcing of deep neural network models.

• Item Type: Book Sections
• Additional Information: This research was supported by Institute for Infocomm Research, an A*STAR research entity, under the RIE2020 Advanced Manufacturing and Engineering (AME) Program (Award no. A19E3b0099).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Keoh, Dr Sye Loong
• Authors: Kang, D. M., Faahym, H., Meftah, S., Keoh, S. L., and Khin, M. M. A.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783031495847