Intel SGX Enclaves Research Articles

Computation on Encrypted Data Using Dataflow Authentication

Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks. However, due to its high computational complexity, its applicability to general-purpose computing remains limited. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data. We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described previously. We implemented two flavors of DFAuth, a Java bytecode-to-bytecode compiler, and an SGX enclave running a small and program-independent trusted code base. We applied DFAuth to a neural network performing machine learning on sensitive medical data and a smart charging scheduler for electric vehicles. Our transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in \( 12.55 \,\mathrm{m}\mathrm{s} \) . Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds.

Communication-efficient outsourced privacy-preserving classification service using trusted processor

Machine learning (ML) classification has been one of the most important techniques of popular Internet services that aim to provide accurate predictions of data by means of a classifier model. In a machine-learning-as-a-service (MLaaS) system, the service provider allows classifier owners to upload their classifier models and charge other users for access on a pay-per-query basis, so that a user can query a classifier with data instances and then obtain their classification results. However, in a traditional way, either stored classifiers or queried data, which are potentially sensitive and economical, will be exposed by the service. Due to privacy concerns, both the classifiers and the data should remain confidential. In this paper, we propose a novel scheme to enable a classifier owner to outsourcely store his/her classifier model on a cloud server for users’ queries, while protecting the confidentiality of classifier and data. We adopt a trusted processor to design efficient classification protocols for two concrete classifiers respectively. For the communicational efficiency, users only need to interact with the server no more than twice for each query in our scheme. We implement the prototype of the scheme and conduct experiments in an Intel SGX enclave. The experimental results show that the scheme is practical.

Secure top most significant genome variants search: iDASH 2017 competition

BackgroundOne of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most significant SNPs (Single-Nucleotide Polymorphisms) in a database of genome records labeled with control or case. In this paper we discuss the solution submitted by our team to this competition.MethodsPrivacy and confidentiality of genome data had to be ensured using Intel SGX enclaves. The typical use-case of this application is the multi-party computation (each party possessing one or several genome records) of the SNPs which statistically differentiate control and case genome datasets.ResultsOur solution consists of two applications: (i) compress and encrypt genome files and (ii) perform genome processing (top most important SNPs search). We have opted for a horizontal treatment of genome records and heavily used parallel processing. Rust programming language was employed to develop both applications.ConclusionsExecution performance of the processing applications scales well and very good performance metrics are obtained. Contest organizers selected it as the best submission amongst other received competition entries and our team was awarded the first prize on this track.