Privacy-preserving sparse representation classification in cloud-enabled mobile applications

Shen, Yiran; Luo, Chengwen; Yin, Dong; Wen, Hongkai; Daniela, Rus; Hu, Wen

doi:10.1016/j.comnet.2018.01.035

Cited by 18 publications

(19 citation statements)

References 20 publications

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“…In this combination, a multi-key homomorphic encryption is combined with double decryption mechanisms, fully homomorphic encryptions, and deep learning schemes to preserve the privacy of encrypted data. In [26], a privacy-preserving sparse representation classification technique was proposed for cloud-enabled mobile applications. In this technique, the privacy of data contributors and application users is protected in the presence of an untrusted cloud server against various types of attacks, such as content privacy attack, source privacy attack, and label privacy attack.…”

Section: B Privacy-preserving Using Machine Learning Methodologiesmentioning

confidence: 99%

“…In this technique, the privacy of data contributors and application users is protected in the presence of an untrusted cloud server against various types of attacks, such as content privacy attack, source privacy attack, and label privacy attack. Schemes presented in [22]- [26] are able to stand against various types of privacy attacks, however, these schemes are not suitable for real-time IoMT applications.…”

Section: B Privacy-preserving Using Machine Learning Methodologiesmentioning

confidence: 99%

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P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Usman

Jan

et al. 2019

IEEE J. Select. Areas Commun.

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The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays, and can be used in various smart city applications, such as traffic management, ehealth, and surveillance. In IoMT applications, multimedia data is generated from devices with sensing capabilities, e.g., Multimedia Sensor Nodes (MSNs). These devices come with limited computational and storage resources, and cannot hold captured multimedia data for a long time if the connection between a base station and cloud server is down. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, such a data collection raise privacy issues, such as revealing identities and location information of MSNs to mobile sinks. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis framework for IoMT applications. The proposed framework distributes an underlying Wireless Multimedia Sensor Networks (WMSNs) into multiple clusters. Each cluster is represented by a cluster head. The cluster heads are responsible to protect the privacy of member nodes through data and location aggregation. Later, aggregated multimedia data is analyzed at cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that our proposed framework outperforms other stateof-the-art techniques, and can be used to collect multimedia data in various IoMT applications.

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Section: B Privacy-preserving Using Machine Learning Methodologiesmentioning

confidence: 99%

Section: B Privacy-preserving Using Machine Learning Methodologiesmentioning

confidence: 99%

P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Usman

Jan

et al. 2019

IEEE J. Select. Areas Commun.

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“…The approach proposed in this paper also belongs to this category. In the following, we review each of [22,29,39] and then discuss our new design to overcome their shortcomings.…”

Section: Training Data Encryption/obfuscationmentioning

confidence: 99%

On lightweight privacy-preserving collaborative learning for internet-of-things objects

Jiang¹,

Tan²,

Lou³

et al. 2019

Proceedings of the International Conference on Internet of Things Design and Implementation

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The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacypreserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resourceconstrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities. CCS CONCEPTS• Computer systems organization → Sensor networks; • Computing methodologies → Supervised learning; • Security and privacy → Domain-specific security and privacy architectures.

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“…These services support human daily life by studying mobility patterns from trillions of trails and footprints [12]. Urban planning [13], face recognition [14], classification [15], traffic forecasting [16], marker campaign [17], prediction of epidemics [18,19], latent data privacy [20], and designing of mobile network protocols [21] are all powered by human mobility trajectories. Some other services exploit the users' daily mobility datasets finding the mobility patterns and mining users' activities to provide useful extensive services [22].…”

Section: Services For the Mobility Datasetsmentioning

confidence: 99%

GANs Based Density Distribution Privacy-Preservation on Mobility Data

Yin

Yang

2018

Security and Communication Networks

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With the development of mobile devices and GPS, plenty of Location-based Services (LBSs) have emerged in these years. LBSs can be applied in a variety of contexts, such as health, entertainment, and personal life. The location based data that contains significant personal information is released for analysing and mining. The privacy information of users can be attacked from the published data. In this paper, we investigate the problem of privacy-preservation of density distribution on mobility data. Different from adding noises into the original data for privacy protection, we devise the Generative Adversarial Networks (GANs) to train the generator and discriminator for generating the privacy-preserved data. We conduct extensive experiments on two real world mobile datasets. It is demonstrated that our method outperforms the differential privacy approach in both data utility and attack error.

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Privacy-preserving sparse representation classification in cloud-enabled mobile applications

Cited by 18 publications

References 20 publications

P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

On lightweight privacy-preserving collaborative learning for internet-of-things objects

GANs Based Density Distribution Privacy-Preservation on Mobility Data

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