Game of Threads

Vicarte, Jose Rodrigo Sanchez; Schreiber, Benjamin; Paccagnella, Riccardo; Fletcher, Christopher W.

doi:10.1145/3373376.3378462

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…It also involves attackers encrypting sensitive or critical data. Previous research has demonstrated that model accuracy may be degraded by just compromising thread scheduling in a multi-threaded ML pipeline [42].…”

Section: Attacks Against Integritymentioning

confidence: 99%

Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

2023

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ML-based applications already play an important role in factories in areas such as visual quality inspection, process optimization, and maintenance prediction and will become even more important in the future. For ML to be used in an industrial setting in a safe and effective way, the different steps needed to use ML must be put together in an ML pipeline. The development of ML pipelines is usually conducted by several and changing external stakeholders because they are very complex constructs, and confidence in their work is not always clear. Thus, end-to-end trust in the ML pipeline is not granted automatically. This is because the components and processes in ML pipelines are not transparent. This can also cause problems with certification in areas where safety is very important, such as the medical field, where procedures and their results must be recorded in detail. In addition, there are security challenges, such as attacks on the model and the ML pipeline, that are difficult to detect. This paper provides an overview of ML security challenges that can arise in production environments and presents a framework on how to address data security and transparency in ML pipelines. The framework is presented using visual quality inspection as an example. The presented framework provides: (a) a tamper-proof data history, which achieves accountability and supports quality audits; (b) an increase in trust by protocol for the used ML pipeline, by rating the experts and entities involved in the ML pipeline and certifying legitimacy for participation; and (c) certification of the pipeline infrastructure, the ML model, data collection, and labelling. After describing the details of the new approach, the mitigation of the previously described security attacks will be demonstrated, and a conclusion will be drawn.

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Section: Attacks Against Integritymentioning

confidence: 99%

Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

2023

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“…In Privman, developers need to separate their applications into a privilege server process and a main application process, in which the main application process only has limited privileges. Apart from software solutions, Intel introduced Software Guard Extensions (SGX) [14] to allow users to define private regions of memory for secured execution, which further inspired tons of security and privacy works [52,56]. However, none of these work explored integrating isolation and compartmentalization into the development flow of AR applications.…”

Section: Protecting Visual Datamentioning

confidence: 99%

LensCap

Iosifescu

LiKamWa

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Augmented Reality (AR) enables smartphone users to interact with virtual content spatially overlaid on a continuously captured physical world. Under the current permission enforcement model in popular operating systems, AR apps are given Internet permission at installation time, and request camera permission and external storage write permission at runtime through a user's approval. With these permissions granted, any Internet-enabled AR app could silently collect camera frames and derived visual information for malicious intent without a user's awareness. This raises serious concerns about the disclosure of private user data in their living environments.To give users more control over application usage of their camera frames and the information derived from them, we introduce LensCap, a split-process app design framework, in which the app is split into a camera-handling visual process and a connectivityhandling network process. At runtime, LensCap manages secured communications between split processes, enacting fine-grained data usage monitoring. LensCap also allows both processes to present interactive user interfaces. With LensCap, users can decide what forms of visual data can be transmitted to the network, while still allowing visual data to be used for AR purposes on device. We prototype LensCap as an Android library and demonstrate its usability as a plugin in Unreal Engine. Performance evaluation results on five AR apps confirm that visual privacy can be preserved with an insignificant latency penalty (< 1.3 ms) at 60 FPS. CCS CONCEPTS• Security and privacy → Software security engineering.

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Blockchain-Empowered Vehicular Intelligence: A Perspective of Asynchronous Federated Learning

Zhang,

2024

IEEE Internet Things M.

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Game of Threads

Cited by 10 publications

References 31 publications

Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

LensCap

Blockchain-Empowered Vehicular Intelligence: A Perspective of Asynchronous Federated Learning

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