Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s to a tool for building real systems today. Over the past decade, MPC has been one of the most active resea...

/pdf/a-pragmatic-introduction-to-secure-multi-party-computation-l30nxb601s.pdf

A Pragmatic Introduction to Secure Multi-Party Computation

Hardware support for trusted execution in modern CPUs enables tenants to shield their data processing workloads in otherwise untrusted cloud environments. Runtime systems for the trusted execution must rely on an interface to the untrusted host OS to use external resources such as storage, network, and other functions. Attackers may exploit this interface to leak data or corrupt the computation. 
We describe SGX-LKL, a system for running Linux binaries inside of Intel SGX enclaves that only exposes a minimal, protected and oblivious host interface: the interface is (i) minimal because SGX-LKL uses a complete library OS inside the enclave, including file system and network stacks, which requires a host interface with only 7 calls; (ii) protected because SGX-LKL transparently encrypts and integrity-protects all data passed via low-level I/O operations; and (iii) oblivious because SGX-LKL performs host operations independently of the application workload. For oblivious disk I/O, SGX-LKL uses an encrypted ext4 file system with shuffled disk blocks. We show that SGX-LKL protects TensorFlow training with a 21% overhead.

SGX-LKL: Securing the Host OS Interface for Trusted Execution.

/pdf/bite-bitcoin-lightweight-client-privacy-using-trusted-2x1kisatin.pdf

BITE: Bitcoin Lightweight Client Privacy using Trusted Execution

The popularity of hardware-based Trusted Execution Environments (TEEs) has recently skyrocketed with the introduction of Intel's Software Guard Extensions (SGX). In SGX, the user process is protected from supervisor software, such as the operating system, through an isolated execution environment called an enclave. Despite the isolation guarantees provided by TEEs, numerous microarchitectural side channel attacks have been demonstrated that bypass their defense mechanisms. But, not all hope is lost for defenders: many modern fine-grain, high-resolution side channels---e.g., execution unit port contention---introduce large amounts of noise, complicating the adversary's task to reliably extract secrets. In this work, we introduce Microarchitectural Replay Attacks, whereby an SGX adversary can denoise nearly arbitrary microarchitectural side channels in a single run of the victim, by causing the victim to repeatedly replay on a page faulting instruction. We design, implement, and demonstrate our ideas in a framework, called MicroScope, and use it to denoise notoriously noisy side channels. Our main result shows how MicroScope can denoise the execution unit port contention channel. Specifically, we show how Micro-Scope can reliably detect the presence or absence of as few as two divide instructions in a single logical run of the victim program. Such an attack could be used to detect subnormal input to individual floating-point instructions, or infer branch directions in an enclave despite today's countermeasures that flush the branch predictor at the enclave boundary. We also use MicroScope to single-step and denoise a cache-based attack on the OpenSSL implementation of AES. Finally, we discuss the broader implications of microarchitectural replay attacks---as well as discuss other mechanisms that can cause replays.

https://dl.acm.org/doi/pdf/10.1145/3307650.3322228

MicroScope: enabling microarchitectural replay attacks

Hardware enclaves such as Intel SGX are a promising technology for improving the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hyper-visor/OS, and encrypt data in RAM. However, for applications that use large amounts of memory, including most databases, enclaves do not protect against access pattern leaks, which let attackers gain a large amount of information about the data. Moreover, the naive way to address this issue, using Oblivious RAM (ORAM) primitives from the security literature, adds substantial overhead.A number of recent works explore trusted hardware enclaves as a path toward secure, access-pattern oblivious outsourcing of data storage and analysis. While these works efficiently solve specific subproblems (e.g. building secure indexes or running analytics queries that always scan entire tables), no prior work has supported oblivious query processing for general query workloads on a DBMS engine with multiple access methods. Moreover, applying these techniques individually does not guarantee that an end-to-end workload, such as a complex SQL query over multiple tables, will be oblivious. In this paper, we introduce ObliDB, an oblivious database engine design that is the first system to provide obliviousness for general database read workloads over multiple access methods.ObliDB introduces a diverse array of new oblivious physical operators to accelerate oblivious SQL queries, giving speedups of up to an order of magnitude over naive ORAM. It supports a broad range of queries, including aggregation, joins, insertions, deletions and point queries. We implement ObliDB and show that, on analytics workloads, ObliDB ranges from 1.1--19x faster than Opaque, a previous oblivious, enclave-based system designed only for analytics, and comes within 2.6 x of Spark SQL, which provides no security guarantees. In addition, ObliDB supports point queries with 3--10ms latency, which is comparable to index-only trusted hardware systems, and runs over 7x faster than HIRB, a previous encryption-based oblivious index system that supports point queries.

/pdf/oblidb-oblivious-query-processing-for-secure-databases-4wjm2t4qj0.pdf

ObliDB: oblivious query processing for secure databases

Search indices are fundamental building blocks of many systems, and there is great interest in running them on encrypted data. Unfortunately, many known schemes that enable search queries on encrypted data achieve efficiency at the expense of security, as they reveal access patterns to the encrypted data. In this paper we present Oblix, a search index for encrypted data that is oblivious (provably hides access patterns), is dynamic (supports inserts and deletes), and has good efficiency. Oblix relies on a combination of novel oblivious-access techniques and recent hardware enclave platforms (e.g., Intel SGX). In particular, a key technical contribution is the design and implementation of doubly-oblivious data structures, in which the client's accesses to its internal memory are oblivious, in addition to accesses to its external memory at the server. These algorithms are motivated by hardware enclaves like SGX, which leak access patterns to both internal and external memory. We demonstrate the usefulness of Oblix in several applications: private contact discovery for Signal, private retrieval of public keys for Key Transparency, and searchable encryption that hides access patterns and result sizes.

/pdf/oblix-an-efficient-oblivious-search-index-3kmd5ibeyp.pdf

Jerry Chen

Papers

Oblix: An Efficient Oblivious Search Index