Challenges For Scaling Applications Across Enclaves

Beekman, Jethro G.; Porter, Donald E.

doi:10.1145/3152701.3152710

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Although they are arguable for performance overhead and trusted computing base size, they are preferred for ease of development and comparable performance. Graphene SGX [159], Occlum [160], Fortanix [161], Anjuna [162], Scone [163] are some examples of lib-OSs that can be used to execute applications on Intel SGX. Another approach for the shared environment considerations could be dividing the data and operations between non-colluding servers, utilizing MPC techniques, into non-colluding servers running on different cloud environments.…”

Section: ) Shared Environmentmentioning

confidence: 99%

A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

et al. 2022

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As machine learning and artificial intelligence (ML/AI) are becoming more popular and advanced, there is a wish to turn sensitive data into valuable information via ML/AI techniques revealing only data that is allowed by concerned parties or without revealing any information about the data to third parties. Collaborative ML approaches like federated learning (FL) help tackle these needs and concerns, bringing a way to use sensitive data without disclosing critically sensitive features of that data. In this paper, we provide a detailed analysis of state of the art for collaborative ML approaches from a privacy perspective. A detailed threat model and security and privacy considerations are given for each collaborative method. We deeply analyze Privacy Enhancing Technologies (PETs), covering secure multi-party computation (SMPC), homomorphic encryption (HE), differential privacy (DP), and confidential computing (CC) in the context of collaborative ML. We introduce a guideline on the selection of the privacy preserving technologies for collaborative ML and privacy practitioners. This study constitutes the first survey to provide an indepth focus on collaborative ML requirements and constraints for privacy solutions while also providing guidelines on the selection of PETs.

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Section: ) Shared Environmentmentioning

confidence: 99%

A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

et al. 2022

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“…Researchers from Intel proposed the use of SGX for securing Barbican [16]. Along the same lines, Fortanix' selfdefending key management service (SDKMS) [44], [6] also uses SGX to securely generate, store and use cryptographic keys, certificates and various types of secrets. Both approaches essentially provide a replacement for the functionality normally provided by HSMs by using enclaves, hence reducing costs and providing better extensibility.…”

Section: Macro-benchmarksmentioning

confidence: 99%

Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders

Gregor¹,

Ozga²,

Vaucher³

et al. 2020

Preprint

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Trust is arguably the most important challenge for critical services both deployed as well as accessed remotely over the network. These systems are exposed to a wide diversity of threats, ranging from bugs to exploits, active attacks, rogue operators, or simply careless administrators. To protect such applications, one needs to guarantee that they are properly configured and securely provisioned with the "secrets" (e.g., encryption keys) necessary to preserve not only the confidentiality, integrity and freshness of their data but also their code. Furthermore, these secrets should not be kept under the control of a single stakeholder-which might be compromised and would represent a single point of failure-and they must be protected across software versions in the sense that attackers cannot get access to them via malicious updates. Traditional approaches for solving these challenges often use ad hoc techniques and ultimately rely on a hardware security module (HSM) as root of trust. We propose a more powerful and generic approach to trust management that instead relies on trusted execution environments (TEEs) and a set of stakeholders as root of trust. Our system, PALAEMON, can operate as a managed service deployed in an untrusted environment, i.e., one can delegate its operations to an untrusted cloud provider with the guarantee that data will remain confidential despite not trusting any individual human (even with root access) nor system software. PALAEMON addresses in a secure, efficient and cost-effective way five main challenges faced when developing trusted networked applications and services. Our evaluation on a range of benchmarks and real applications shows that PALAEMON performs efficiently and can protect secrets of services without any change to their source code.

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Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders

Gregor

Ozga

Vaucher

et al. 2020

2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Challenges For Scaling Applications Across Enclaves

Cited by 6 publications

References 5 publications

A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders

Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders

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