scholarly journals Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986100 ◽  
Author(s):  
Liang Liu ◽  
Zhenhai Hu ◽  
Lisong Wang
Keyword(s):  
Sensor Networks ◽  
Query Processing ◽  
Energy Efficient ◽  
Privacy Protection ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Sensory Data ◽  
Spatial Range ◽  
Query Algorithm

The existing privacy-preserving aggregation query processing methods in sensor networks rely on pre-established network topology and require all nodes in the network to participate in query processing. Maintaining the topology results in a large amount of energy overhead, and in many cases, the user is interested only in the aggregated query results of some areas in the network, and thus, the participation of the entire network node is not necessary. Aiming to solve this problem, this article proposes a spatial range aggregation query algorithm for a dynamic sensor network with privacy protection (energy-efficient privacy-preserving data aggregation). The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. The analysis and experimental results show that the proposed algorithm outperforms the existing algorithms in terms of energy and privacy protection.

scholarly journals Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Mingshan Xie ◽  
Yong Bai ◽  
Mengxing Huang ◽  
Zhuhua Hu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
Privacy Preserving ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Fusion Data ◽  
Data Privacy Protection

Privacy-preserving in wireless sensor networks is one of the key problems to be solved in practical applications. It is of great significance to solve the problem of data privacy protection for large-scale applications of wireless sensor networks. The characteristics of wireless sensor networks make data privacy protection technology face serious challenges. At present, the technology of data privacy protection in wireless sensor networks has become a hot research topic, mainly for data aggregation, data query, and access control of data privacy protection. In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. In multiple stages and multiple aspects, the difficulty of cracking and crack costs are increased. The simulation results demonstrate that, compared with the typical Slice-Mix-AggRegaTe (SMART) algorithm, the algorithm proposed in this paper has a better data privacy-preserving ability when the traffic load is not very heavy.

scholarly journals EPCT: An Efficient Privacy-Preserving and Collusion-Resisting Top- k Query Processing in WSNs

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qian Zhou ◽  
Hua Dai ◽  
Jianguo Zhou ◽  
Rongqi Qi ◽  
Geng Yang ◽  
...  
Keyword(s):  
Query Processing ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Experimental Result ◽  
Communication Overhead ◽  
Symmetric Encryption ◽  
Sensory Data ◽  
Two Phases ◽  
Secure Query Processing

Data privacy threat arises during providing top- k query processing in the wireless sensor networks. This article presents an efficient privacy-preserving and collusion-resisting top- k (EPCT) query processing protocol. A minimized candidate encrypted dataset determination model is first designed, which is the foundation of EPCT. The model guides the idea of query processing and guarantees the correctness of the protocol. The symmetric encryption with different private key in each sensor is deployed to protect the privacy of sensory data even a few sensors in the networks have been colluding with adversaries. Based on the above model and security setting, two phases of interactions between the interested sensors and the sink are designed to implement the secure query processing protocol. The security analysis shows that the proposed protocol is capable of providing secure top- k queries in the manner of privacy protection and anticollusion, whereas the experimental result indicates that the protocol outperforms the existing works on communication overhead.

scholarly journals Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  
Keyword(s):  
Data Privacy ◽  
Medical Training ◽  
External Validation ◽  
Mainland China ◽  
Privacy Preserving ◽  
Generalization Capability ◽  
Major Focus ◽  
Sensitive Data ◽  
Lung Abnormalities ◽  
Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

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