PPO-CPQ: A Privacy-Preserving Optimization of Clinical Pathway Query for E-Healthcare Systems

Zhang, Mingwu; Yu, Chiang; Susilo, Willy

doi:10.1109/jiot.2020.3007518

Cited by 116 publications

(67 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many general datasets are currently connected to image segmentation, such as PASCAL VOC, MS COC, ADE20K, and, in the field of the autonomous driving area, Cityscapes [ 11 ] and KITTI ([ 9 , 10 ]). The challenge of visual object classes, or VOCs, consists of two components [ 15 ]: (1) image and annotation datasets that are available to the public and (2) annual workshops and competitions that are held online on some websites and sometimes in person. The main challenges have been dealt with since 2005.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

“…This information is accessible to the public [ 14 ]. In the context of e-healthcare, Zhang et al showed a privacy-preserving optimization of the clinical pathway query method (PPO-CPQ) [ 15 ]. Ala et al have used a metaheuristic algorithm and optimized an appointment scheduling issue for healthcare systems depending on the quality of fairness service [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

Valizadeh

Shariatee

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Image medical semantic segmentation has been employed in various areas, including medical imaging, computer vision, and intelligent transportation. In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method. The traditional method and the published dataset for segmentation are reviewed in the first step. The presented aspects, including all-convolution network, sampling methods, FCN connector with CRF methods, extended convolutional neural network methods, improvements in network structure, pyramid methods, multistage and multifeature methods, supervised methods, semiregulatory methods, and nonregulatory methods, are then thoroughly explored in current methods based on the deep neural network. Finally, a general conclusion on the use of developed advances based on deep neural network concepts in semantic segmentation is presented.

show abstract

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

Valizadeh

Shariatee

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…However, the implementation of GVA is NP-hard even under the assumption of single-minded bidders. Zhang et al [23] investigated the impact on such mechanisms of replacing exact solutions by approximate ones and proposed a particular greedy optimization method, which could guarantee the truthfulness of the auction.…”

Section: Background and Related Workmentioning

confidence: 99%

PP-VCA: A Privacy-Preserving and Verifiable Combinatorial Auction Mechanism

Zhang

Zhou

2020

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Combinatorial auctions can be employed in the fields such as spectrum auction, network routing, railroad segment, and energy auction, which allow multiple goods to be sold simultaneously and any combination of goods to be bid and the maximum sum of combinations of bidding prices to be calculated. However, in traditional combinatorial auction mechanisms, data concerning bidders’ price and bundle might reveal sensitive information, such as personal preference and competitive relation since the winner determination problem needs to be resolved in terms of sensitive data as above. In order to solve this issue, this paper exploits a privacy-preserving and verifiable combinatorial auction protocol (PP-VCA) to protect bidders’ privacy and ensure the correct auction price in a secure manner, in which we design a one-way and monotonically increasing function to protect a bidder’s bid to enable the auctioneer to pick out the largest bid without revealing any information about bids. Moreover, we design and employ three subprotocols, namely, privacy-preserving winner determination protocol, privacy-preserving scalar protocol, and privacy-preserving verifiable payment determination protocol, to implement the combinatorial auction with bidder privacy and payment verifiability. The results of comprehensive experimental evaluations indicate that our proposed scheme provides a better efficiency and flexibility to meet different types of data volume in terms of the number of goods and bidders.

show abstract

“…The main reason of PSI cardinality has been widely studied is that it has many real applications. Such as, PSI cardinality has been used in privacy preserving data mining 3 , information-sharing 4 , human genome research 5 , national security 6 , Botnet identification 7 , medical data preserving 8 , 9 , social networks 10 , 11 , location privacy protecting 12 , 13 , searchable encryption scheme 14 and anonymous authentication 15 , 16 . In recent years more and more PSI cardinality protocols are proposed, e.g.…”

Section: Introductionmentioning

confidence: 99%

Quantum private set intersection cardinality based on bloom filter

Liu

Ruan

Shi

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Private Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. Thus it is more likely to realize through the present technologies. The validity of the protocol is verified by comparing with other protocols. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. Therefore, the protocol has a good prospects in dealing with big data, privacy-protection and information-sharing, such as the patient contact for COVID-19.

show abstract

PPO-CPQ: A Privacy-Preserving Optimization of Clinical Pathway Query for E-Healthcare Systems

Cited by 116 publications

References 36 publications

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

PP-VCA: A Privacy-Preserving and Verifiable Combinatorial Auction Mechanism

Quantum private set intersection cardinality based on bloom filter

Contact Info

Product

Resources

About