SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications

Alam, Afshar; Sharma, Sanjay; Chen, Keke

doi:10.2478/popets-2021-0002

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…TEE-based approaches TEE-based privacy-preserving frameworks become popular in the last few years. Researchers have been exploring TEE-based applications for mainly (i) data-intensive analytics (Dinh et al, 2015;Schuster et al, 2015;Zheng et al, 2017;Alam et al, 2021) and (ii) data management (Priebe et al, 2018;Antonopoulos et al, 2020;Sun et al, 2021). VC3 (Schuster et al, 2015) and M2R (Dinh et al, 2015) extends and utilizes the Hadoop System, where the most sensitive part of the computation takes place in TEE.…”

Section: Related Work Cryptographic Approachesmentioning

confidence: 99%

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

Alam,

Chen

2023

Front. Big Data

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IntroductionBig graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors.MethodsWe propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers.ResultsThe TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 103−105times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost.DiscussionOur experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection.

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Section: Related Work Cryptographic Approachesmentioning

confidence: 99%

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

Alam,

Chen

2023

Front. Big Data

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“…However, the authors acknowledge that their solution is not suitable for all use cases, requiring significant overhead, and may be seen as a first step for further research. The research of [40] goes in a similar direction by looking at how best to protect privacy of access patterns in Intel SGX TEE. The authors agree that ORAM provides a very expensive solution and propose a different framework, called SGX-MR (MapReduce) that works for many data-intensive applications.…”

Section: Trusted Execution Environmentmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data. For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.

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“…Memory side channels and cache side channels are the two types that researchers mostly examined. Memory side-channel attacks are primarily access pattern attacks (Sasy et al 2018;Ahmad et al 2018;Shinde et al 2016). As the encrypted data have to be loaded from the file to the untrusted area first and then accessed by the enclave, the access pattern attacks seem inevitable for data-intensive applications like CML.…”

Section: Hardware-assisted Approachesmentioning

confidence: 99%

“…As the encrypted data have to be loaded from the file to the untrusted area first and then accessed by the enclave, the access pattern attacks seem inevitable for data-intensive applications like CML. The well-known approach addressing this problem is the Oblivious RAM technique (Goldreich and Ostrovsky 1996), which has been applied to SGX by ZeroTrace (Sasy et al 2018) and Obliviate (Ahmad et al 2018). Ohrimenko et al (2016) also uses oblivious access techniques for multi-party machine learning with SGX.…”

Section: Hardware-assisted Approachesmentioning

confidence: 99%

“…Ohrimenko et al (2016) also uses oblivious access techniques for multi-party machine learning with SGX. Branching attacks (Shinde et al 2016) utilize the branching statements and manipulate page faults to extract information, often addressable with oblivious branching instructions such as CMOV (Shinde et al 2016;Sasy et al 2018;Alam et al). Cache side-channel attacks such as cache timing and transient execution state (Bulck et al 2018;Ristenpart et al 2009;Kocher et al 2019;Lipp et al 2018) utilize the unique CPU architectural features and thus depend on the manufacturers' firmware and software patches to fix.…”

Section: Hardware-assisted Approachesmentioning

confidence: 99%

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Confidential machine learning on untrusted platforms: a survey

Sharma

Chen

2021

Cybersecur

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With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.

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SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications

Cited by 6 publications

References 25 publications

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Confidential machine learning on untrusted platforms: a survey

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