A Lightweight Privacy-Preserving CNN Feature Extraction Framework for Mobile Sensing

Huang, Kai; Liu, Ximeng; Fu, Shaojing; Guo, Deke; Xu, Ming

doi:10.1109/tdsc.2019.2913362

Cited by 87 publications

(67 citation statements)

References 46 publications

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“…Discussion. There already exist several protocols about privacy-preserving comparison of two additive shared integers [20][21][22]32]. First, we should note that the setting of these comparisons is different from ours.…”

Section: Secure Comparison Algorithmmentioning

confidence: 90%

“…We newly design a secure comparison protocol that can return additive shares of the comparison result on additively secret shared inputs. Compared with the Huang et al's work [21] and Zheng et al's work [20], the number of additive multiplications required can be reduced from 2l and 3l to l respectively, where l is the bit-length of a feature vector's element. Compared with Liu et al's work [22], which is based on additive secret sharing and additively homomorphic cryptosystem, the proposed work is more secure and efficient.…”

Section: Our Contributionsmentioning

confidence: 97%

“…First, the two parties should share pre-computed triples which can be denoted as c = a • b . The Beaver triples can be generated by the two parties by Oblivious Transfer [27] or distributed from a trusted third party [21]. Suppose that the to be multiplied integers are x and y.…”

Section: Multiplication Of Additive Sharesmentioning

confidence: 99%

“…After running this secure multiplication protocol, the two parties obtain x • y . More details about the multiplication of additive shares can be found in references [21,22].…”

Section: Multiplication Of Additive Sharesmentioning

confidence: 99%

See 3 more Smart Citations

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Liu

Zhao

et al. 2020

Symmetry

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With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.

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Section: Secure Comparison Algorithmmentioning

confidence: 90%

Section: Our Contributionsmentioning

confidence: 97%

Section: Multiplication Of Additive Sharesmentioning

confidence: 99%

“…After running this secure multiplication protocol, the two parties obtain x • y . More details about the multiplication of additive shares can be found in references [21,22].…”

Section: Multiplication Of Additive Sharesmentioning

confidence: 99%

See 2 more Smart Citations

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Liu

Zhao

et al. 2020

Symmetry

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show abstract

“…In addition, there is also document discussing the security challenges faced by edge computing, such as how to ensure privacy and security when mobile devices belonging to users who do not trust each other participate in public computing. The work in [6] proposed a new lightweight framework based on edge computing for CNN feature extraction of mobile sensing. The framework is able to significantly reduce the latency and overhead of end devices while protecting privacy.…”

Section: A Machine Behavior and Ai Cloud Computingmentioning

confidence: 99%

Robot Path Planning Agent for Evaluating Collaborative Machine Behavior

Ma¹,

Yang²,

Wei³

et al. 2019

Preprint

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We first review the literature on machine behavior which may contain the interaction between machines, and then discuss the methodology and the related case involving collaboration between robots to instantiate the concept with AI empowerment. The navigation case aims to a security application with autonomous control and roadside agent embedded in a mobile edge computing structure. In the studied navigation based on the artificial potential field method, robots need to use position information to calculate the moving direction frequently. In the case of high motion speed as well as GPS positioning error, the path trajectory may show a sharp change of direction. In order to mitigate the trajectory oscillation, this paper proposes a path planning design where training process and motion direction prediction are integrated by using artificial neural network. The auxiliary navigation agent near multiple obstacles can first extract the past movement information of the robot and then determines whether there is a serious path jitter event. Computer simulation analysis shows that the combination of autonomous control and cooperative behavior can effectively reduce the path jitter so as to achieve a fast and safe path planning.

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Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human–Machine Interfaces

Fang

Wang

et al. 2023

Advanced Science

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Recent advances in flexible wearable devices have boosted the remarkable development of devices for human-machine interfaces, which are of great value to emerging cybernetics, robotics, and Metaverse systems. However, the effectiveness of existing approaches is limited by the quality of sensor data and classification models with high computational costs. Here, a novel gesture recognition system with triboelectric smart wristbands and an adaptive accelerated learning (AAL) model is proposed. The sensor array is well deployed according to the wrist anatomy and retrieves hand motions from a distance, exhibiting highly sensitive and high-quality sensing capabilities beyond existing methods. Importantly, the anatomical design leads to the close correspondence between the actions of dominant muscle/tendon groups and gestures, and the resulting distinctive features in sensor signals are very valuable for differentiating gestures with data from 7 sensors. The AAL model realizes a 97.56% identification accuracy in training 21 classes with only one-third operands of the original neural network. The applications of the system are further exploited in real-time somatosensory teleoperations with a low latency of <1 s, revealing a new possibility for endowing cyber-human interactions with disruptive innovation and immersive experience.

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A Lightweight Privacy-Preserving CNN Feature Extraction Framework for Mobile Sensing

Cited by 87 publications

References 46 publications

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Robot Path Planning Agent for Evaluating Collaborative Machine Behavior

Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human–Machine Interfaces

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