Machine Learning Differential Privacy With Multifunctional Aggregation in a Fog Computing Architecture

Yang, Mengmeng; Zhu, Tianqing; Liu, Bo; Xiang, Yang; Zhou, Wanlei

doi:10.1109/access.2018.2817523

Cited by 59 publications

(41 citation statements)

References 22 publications

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“…road units) collect and preprocess traffic flow data from multiple vehicles and then send them to a cloud server. Owing to privacy concern, homomorphic encryption [77]- [79] and differential privacy [42], [80] were applied to achieve privacy-preserving data aggregation in edge computing.…”

Section: B Secure Data Processing In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

127

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Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications such as environmental monitoring, smart navigation and smart healthcare come with new requirements such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

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Section: B Secure Data Processing In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

127

View full text Add to dashboard Cite

show abstract

“…Therefore, the requirement of middleware technology using edge computing is put forward to achieve cross-layer context sharing and localized context processing in smart home scenarios. Fog computing (10) viewed as SDN middleware inherits the advantages of not only cloud computing but also edge computing, which can give full play to the cross-layer capability and provide an effective way for local context processing in home scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Ru¹,

Chu²,

Zhang³

et al. 2019

Sensors and Materials

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“…It employs a differential privacy model to resist differential attacks that most existing data aggregation schemes have suffered from. Yang et al in [14] also proposed a differential privacy model based on machine learning algorithms. The model can reduce communication overhead as well as protect the privacy of sensitive data rigorously for the fog computing architecture.…”

Section: Related Workmentioning

confidence: 99%

“…We compare the performance of the proposed Re-ADP strategy with MLDP in [14] and the RescueDP strategy in [26] over two real datasets. The MLDP is a privacy-preserving data aggregation scheme under fog computing based on machine learning, while the RescueDP is the latest strategy that provides -event privacy for realtime aggregate data publishing.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Compared with the traditional cryptography-based schemes, differential privacy can protect individuals privacy while improving data accuracy as much as possible. For example, the authors of [14] protect privacy of aggregated data with differential privacy by using machine learning. Although there exist many studies based on differential privacy, some challenges cannot be addressed.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

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Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

Huo

Yong

2018

Wireless Communications and Mobile Computing

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In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive -event differential privacy (Re-ADP). Based on adaptive -event differential privacy, the framework can protect any data collected by sensors over any dynamic time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

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Machine Learning Differential Privacy With Multifunctional Aggregation in a Fog Computing Architecture

Cited by 59 publications

References 22 publications

A Survey on Secure Data Analytics in Edge Computing

A Survey on Secure Data Analytics in Edge Computing

A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

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