Exploring the use of Intel SGX for Secure Many-Party Applications

Küçük, Kubilay Ahmet; Paverd, Andrew; Martin, Andrew P.; Asokan, N.; Simpson, Andrew; Ankele, Robin

doi:10.1145/3007788.3007793

Cited by 28 publications

(15 citation statements)

References 20 publications

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“…The authors utilized trusted computing to mitigate the cyber security and privacy threats in the next generation energy networks. Considering SG as case study, similar work is also presented in [215] [217]. However, such techniques (TRE) could suffer from single point of attack as pointed out in [181].…”

Section: F Advanced Trusted Systems Based Privacy-preserving Mechanismsmentioning

confidence: 95%

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Kumar

Lin

Bai

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

262

115

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Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring and managing multiple domains in the SG. Despite the salient features of SMI, security and privacy issues have been under debate because of the large number of heterogeneous devices that are anticipated to be coordinated through public communication networks. This survey paper shows a brief overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network. Specifically, we present a threat taxonomy considering: (i) threats in system-level security, (ii) threats and/or theft of services, and (iii) threats to privacy. Based on the presented threats, we derive a set of security and privacy requirements for SG metering networks. Furthermore, we discuss various schemes that have been proposed to address these threats, considering the pros and cons of each. Finally, we investigate the open research issues to shed new light on future research directions in smart grid metering networks.

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Section: F Advanced Trusted Systems Based Privacy-preserving Mechanismsmentioning

confidence: 95%

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Kumar

Lin

Bai

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

262

115

View full text Add to dashboard Cite

show abstract

“…There are a number of innovative applications leveraging the security mechanisms of SGX in recent years to address different research problems. SGX was used to replace cryptographic primitives such as efficient two-party secure function evaluation [46], private membership test [47], and trustworthy remote entity [48]. SGX was also adopted for sensitive data analytics, processing, and search, e.g., VC3 [49], Opaque [50], SecureKeeper [51], PROCHLO [52], SafeBricks [53], Oblix [54], and HardIDX [55].…”

Section: Related Workmentioning

confidence: 99%

Reaching Data Confidentiality and Model Accountability on the CalTrain

Gu¹,

Jamjoom²,

Su³

et al. 2019

2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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Distributed collaborative learning (DCL) paradigms enable building joint machine learning models from distrusting multi-party participants. Data confidentiality is guaranteed by retaining private training data on each participant's local infrastructure. However, this approach to achieving data confidentiality makes today's DCL designs fundamentally vulnerable to data poisoning and backdoor attacks. It also limits DCL's model accountability, which is key to backtracking the responsible "bad" training data instances/contributors. In this paper, we introduce CALTRAIN 1 , a Trusted Execution Environment (TEE) based centralized multi-party collaborative learning system that simultaneously achieves data confidentiality and model accountability. CALTRAIN enforces isolated computation on centrally aggregated training data to guarantee data confidentiality. To support building accountable learning models, we securely maintain the links between training instances and their corresponding contributors. Our evaluation shows that the models generated from CALTRAIN can achieve the same prediction accuracy when compared to the models trained in non-protected environments. We also demonstrate that when malicious training participants tend to implant backdoors during model training, CALTRAIN can accurately and precisely discover the poisoned and mislabeled training data that lead to the runtime mispredictions.

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“…TEE. SMPC via TEEs has been proposed in [12,13,14]. Ohrimenko et al [15] adapt several machine learning algorithms, including neural networks, to prevent cache-based side-channel attacks in scenarios where multiple institutions use Intel SGX to securely share their datasets for training and evaluation of joint machine learning models.…”

Section: Privacy-preserving Machine Learningmentioning

confidence: 99%

VoiceGuard: Secure and Private Speech Processing

Brasser

Frassetto

Riedhammer³

et al. 2018

Interspeech 2018

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With the advent of smart-home devices providing voice-based interfaces, such as Amazon Alexa or Apple Siri, voice data is constantly transferred to cloud services for automated speech recognition or speaker verification. While this development enables intriguing new applications, it also poses significant risks: Voice data is highly sensitive since it contains biometric information of the speaker as well as the spoken words. This data may be abused if not protected properly, thus the security and privacy of billions of end-users is at stake. We tackle this challenge by proposing an architecture, dubbed VoiceGuard, that efficiently protects the speech processing task inside a trusted execution environment (TEE). Our solution preserves the privacy of users while at the same time it does not require the service provider to reveal model parameters. Our architecture can be extended to enable user-specific models, such as feature transformations (including fMLLR), i-vectors, or model transformations (e.g., custom output layers). It also generalizes to secure on-premise solutions, allowing vendors to securely ship their models to customers. We provide a proof-of-concept implementation and evaluate it on the Resource Management and WSJ speech recognition tasks isolated with Intel SGX, a widely available TEE implementation, demonstrating even real time processing capabilities.

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Exploring the use of Intel SGX for Secure Many-Party Applications

Cited by 28 publications

References 20 publications

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Reaching Data Confidentiality and Model Accountability on the CalTrain

VoiceGuard: Secure and Private Speech Processing

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