The theoretical construct of a Trusted Third Party (TTP) has the potential to solve many security and privacy challenges. In particular, a TTP is an ideal way to achieve secure multiparty computation-a privacy-enhancing technique in which mutually distrusting participants jointly compute a function over their private inputs without revealing these inputs. Although there exist cryptographic protocols to achieve this, their performance often limits them to the two-party case, or to a small number of participants. However, many real-world applications involve thousands or tens of thousands of participants. Examples of this type of many-party application include privacy-preserving energy metering, location-based services, and mobile network roaming. Challenging the notion that a trustworthy TTP does not exist, recent research has shown how trusted hardware and remote attestation can be used to establish a sufficient level of assurance in a real system such that it can serve as a trustworthy remote entity (TRE). We explore the use of Intel SGX, the most recent and arguably most promising trusted hardware technology, as the basis for a TRE for many-party applications. Using privacy-preserving energy metering as a case study, we design and implement a prototype TRE using SGX, and compare its performance to a previous system based on the Trusted Platform Module (TPM). Our results show that even without specialized optimizations, SGX provides comparable performance to the optimized TPM system, and therefore has significant potential for large-scale many-party applications.
The significant improvements in technology that have been seen in recent years have resulted in a shift in the computing paradigm: from isolated computational tasks to distributed tasks executed in multi-party settings. Secure Multi-Party Computation (MPC) allows for multiple parties to jointly compute a function on their private inputs. Unfortunately, traditional MPC algorithms are inefficient in the presence of a large number of participants. Moreover, in the traditional setting, MPC is only concerned with privacy of the input values. However, there is often a need to preserve the privacy of individuals on the basis of the output of the computation. Techniques proposed by the Trusted Computing community have shown promise in the context of new secure and efficient large-scale applications. In this paper, we define and analyse several use cases related to large-scale applications of the MPC paradigm. From these use cases, we derive requirements and criteria to evaluate certain MPC protocols used for largescale applications. Furthermore, we propose the utilisation of a Trustworthy Remote Entity and privacy-preserving algorithms to achieve confidentiality and privacy in such settings.
Many applications are built upon private algorithms, and executing them in untrusted, remote environments poses confidentiality issues. To some extent, these problems can be addressed by ensuring the use of secure hardware in the execution environment; however, an insecure software-stack can only provide limited algorithm secrecy. This paper aims to address this problem, by exploring the components of the Trusted Computing Base (TCB) in hardware-supported enclaves. First, we provide a taxonomy and give an extensive understanding of trade-offs during secure enclave development. Next, we present a case study on existing secret-code execution frameworks; which have bad TCB design due to processing secrets with commodity software in enclaves. This increased attack surface introduces additional footprints on memory that breaks the confidentiality guarantees; as a result, the private algorithms are leaked. Finally, we propose an alternative approach for remote secret-code execution of private algorithms. Our solution removes the potentially untrusted commodity software from the TCB and provides a minimal loader for secret-code execution. Based on our new enclave development paradigm, we demonstrate three industrial templates for cloud applications: 1 computational power as a service, 2 algorithm querying as a service, and 3 data querying as a service.
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