Journal ArticleDOI
Privacy-Preserving Distributed Multi-Task Learning against Inference Attack in Cloud Computing
TLDR
In this paper, a machine learning as a service (MLaaS) has recently been valued by the organizations for machine learning training over SaaS over a period of time.Abstract:
Because of the powerful computing and storage capability in cloud computing, machine learning as a service (MLaaS) has recently been valued by the organizations for machine learning training over s...read more
Citations
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Proceedings ArticleDOI
The Promising Role of Representation Learning for Distributed Computing Continuum Systems
TL;DR: This paper discusses the promising role of ReL for DCCS in terms of different aspects, including device condition monitoring, predictions, management of the systems, etc, and provides a list of Re L algorithms and their pitfalls which helps D CCS by considering various constraints.
Journal ArticleDOI
DisBezant: Secure and Robust Federated Learning Against Byzantine Attack in IoT-Enabled MTS
TL;DR: DisBezant as mentioned in this paper proposes a credibility-based mechanism to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships.
Journal ArticleDOI
DisBezant: Secure and Robust Federated Learning Against Byzantine Attack in IoT-Enabled MTS
TL;DR: DisBezant as mentioned in this paper proposes a credibility-based mechanism to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships.
Proceedings ArticleDOI
The Promising Role of Representation Learning for Distributed Computing Continuum Systems
TL;DR: In this paper , the authors discuss the promising role of ReL for DCCS in terms of different aspects, including device condition monitoring, predictions, management of the systems, etc.
Journal ArticleDOI
Governance and sustainability of distributed continuum systems: a big data approach
TL;DR: In this paper , a general governance and sustainable architecture for distributed computing continuum systems (DCCS) is proposed, which reflects the human body's self-healing model, and the proposed model has three stages: first, it analyzes system data to acquire knowledge; second it can leverage the knowledge to monitor and predict future conditions; and third it takes further actions to autonomously solve any issue or to alert administrators.
References
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Journal ArticleDOI
Privacy-Preserving Deep Learning via Weight Transmission
Le Trieu Phong,Tran Thi Phuong +1 more
TL;DR: In this article, the authors consider the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local datasets owing to privacy concerns.
Journal ArticleDOI
Multi-User Multi-Keyword Rank Search Over Encrypted Data in Arbitrary Language
TL;DR: A new MRSE system is proposed which overcomes almost all the defects of the KNN-SE based MRSE systems, does not require a predefined keyword set and supports keywords in arbitrary languages, is a multi-user system which supports flexible search authorization and time-controlled revocation, and achieves better data privacy protection.
Journal ArticleDOI
ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks
TL;DR: This paper proposes a novel decentralized framework, namely ARMOR, which utilizes OSN users’ social attributes and trust relationships to achieve the friend recommendation in a privacy-preserving manner, and adopts a light-weight privacy- Preserving protocol.
Proceedings ArticleDOI
Distributed Multi-Task Relationship Learning
TL;DR: This paper proposes a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm and proposes a communication-efficient primal-dual distributed optimization algorithm to solve theDual problem by carefully designing local subproblems to make the dual problem decomposable.
Journal ArticleDOI
APPLET: a privacy-preserving framework for location-aware recommender system
TL;DR: This paper proposes a novel framework, namely APPLET, for protecting user privacy information, including locations and recommendation results, within a cloud environment, and theoretically proves that user information is private and will not be leaked during a recommendation.