No abstract

Machine learning has become a critical component of modern datadriven online services. Typically, the training phase of machine learning techniques requires to process large-scale datasets which may contain private and sensitive information of customers. This imposes significant security risks since modern online services rely on cloud computing to store and process the sensitive data. In the untrusted computing infrastructure, security is becoming a paramount concern since the customers need to trust the thirdparty cloud provider. Unfortunately, this trust has been violated multiple times in the past.To overcome the potential security risks in the cloud, we answer the following research question: how to enable secure executions of machine learning computations in the untrusted infrastructure? To achieve this goal, we propose a hardware-assisted approach based on Trusted Execution Environments (TEEs), specifically Intel SGX, to enable secure execution of the machine learning computations over the private and sensitive datasets. More specifically, we propose a generic and secure machine learning framework based on Tensorflow, which enables secure execution of existing applications on the commodity untrusted infrastructure. In particular, we have built our system called TensorSCONE from ground-up by integrating TensorFlow with SCONE, a shielded execution framework based on Intel SGX. The main challenge of this work is to overcome the architectural limitations of Intel SGX in the context of building a secure TensorFlow system. Our evaluation shows that we achieve reasonable performance overheads while providing strong security properties with low TCB.

Shielded execution based on Intel SGX provides strong security guarantees for legacy applications running on untrusted platforms. However, memory safety attacks such as Heartbleed can render the confidentiality and integrity properties of shielded execution completely ineffective. To prevent these attacks, the state-of-the-art memory-safety approaches can be used in the context of shielded execution.In this work, we first showcase that two prominent softwareand hardware-based defenses, AddressSanitizer and Intel MPX respectively, are impractical for shielded execution due to high performance and memory overheads. This motivated our design of SGXBOUNDS-an efficient memory-safety approach for shielded execution exploiting the architectural features of Intel SGX. Our design is based on a simple combination of tagged pointers and compact memory layout.We implemented SGXBOUNDS based on the LLVM compiler framework targeting unmodified multithreaded applications. Our evaluation using Phoenix, PARSEC, and RIPE benchmark suites shows that SGXBOUNDS has performance and memory overheads of 17% and 0.1% respectively, while providing security guarantees similar to AddressSanitizer and Intel MPX. We have obtained similar results with SPEC CPU2006 and four real-world case studies: SQLite, Memcached, Apache, and Nginx.

No abstract

Middleboxes that process confidential data cannot be securely deployed in untrusted cloud environments. To securely outsource middleboxes to the cloud, state-of-the-art systems advocate network processing over the encrypted traffic. Unfortunately, these systems support only restrictive functionalities, and incur prohibitively high overheads.This motivated the design of ShieldBox-a secure middlebox framework for deploying high-performance network functions (NFs) over untrusted commodity servers. Shield-Box securely processes encrypted traffic inside a secure container by leveraging shielded execution. More specifically, ShieldBox builds on hardware-assisted memory protection based on Intel SGX to provide strong confidentiality and integrity guarantees. For middlebox developers, ShieldBox exposes a generic interface based on Click to design and implement a wide-range of NFs using its out-of-the-box elements and C++ extensions. For network operators, ShieldBox provides configuration and attestation service for seamless and verifiable deployment of middleboxes. We have implemented ShieldBox supporting important end-to-end features required for secure network processing, and performance optimizations. Our extensive evaluation shows that ShieldBox achieves a near-native throughput and latency to securely process confidential data at line rate. CCS Concepts• Networks → Middle boxes / network appliances;