Cross-domain dynamic anonymous authenticated group key management with symptom-matching for e-health social system

Yang, Yang; Zheng, Xianghan; Liu, Ximeng; Zhong, Shangping; Chang, Victor

doi:10.1016/j.future.2017.06.025

Cited by 45 publications

(21 citation statements)

References 23 publications

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“…(2) for i = 1 to n (3) for j = 1 to n (4) Determine the weight of edge from node vi to node vj based on the amount of connections of node vi. (5) end for (6) end for (7) for i = 1 to n (8) Set the weight of node vi in the initial stage as 1.…”

Section: Opinion Leader Election Methodsmentioning

confidence: 99%

Deep Community Detection Method for Social Networks

Wu¹,

Q²,

Chen³

2019

Preprint

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With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

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Section: Opinion Leader Election Methodsmentioning

confidence: 99%

Deep Community Detection Method for Social Networks

Wu¹,

Q²,

Chen³

2019

Preprint

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“…A large number of authentication key exchange protocols have been proposed subsequently [2][3][4][5], as well as corresponding applications [6][7][8][9][10]. According to different application scenarios and assumptions, the authentication key exchange protocols are broadly divided into the following two categories: one assumes that each interacting party has a high-entropy private key which can be used to generate a high-entropy session key; the other one assumes that each interacting party only shares a weak password and generates a high-entropy session key through interaction.…”

Section: Introductionmentioning

confidence: 99%

A New Password- and Position-Based Authenticated Key Exchange

Jia¹,

Qiao

Cao³

et al. 2021

Security and Communication Networks

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Password-based authenticated key exchange is a popular method for secure authentication and key exchange. With the wide application of unmanned aerial vehicles, position information has also become an important factor in authentication. In this paper, we present a new key exchange protocol, which firstly realizes dual authentication for both password and position, and we propose two applicable scenarios for the PPAKE mechanism: one is unmanned aerial vehicle authentication, and the other one is authentication in the military base. By adding position authentication, the reliability of authentication has improved, and the difficulty of adversarial attacks also increases. Any arbitrary adversary who can listen, tamper, and send messages can only perform an online attack for password guessing at a specified position. Finally, we provide security proofs under the defined model.

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“…The rapid development of Internet of Things (IoT) [1,2] has greatly changed our daily life, especially in the electronic health (e-health) domain [3]. The patients with chronic diseases or severe illnesses are equipped with implanted or on the surface medical sensors to monitor various kinds of physiological data.…”

Section: Introductionmentioning

confidence: 99%

“…If S satisfies (A, ρ), U utilizes his secret key SK U and LSSS scheme to decrypt Υ and recover M .Otherwise, the algorithm outputs ⊥.Algorithm 11: EHR Decryption AlgorithmInput: P KP A, SKU with attribute set S, CT with access policy (A, ρ).Output: M/⊥. 1 if S satisfies (A, ρ) then 2Data user utilizes the LSSS scheme to find {λi ∈ Zp} i∈[n 1 ] such that i∈[n 1 ] λiAi = (1, 0, · · · , 0);3 Calculate Υ = C0/[e(C1, dU,4) · e(wP A,3, i∈[n 1 ] (dU,5,i) C 2,i ·λ i )]; M = SDec(CM , H0(Υ)); Return M/⊥.4.12. Keyword Match based Policy Update QueryIf the patient P A wants to update the access policy of the EHR ciphertext that are stored in the e-health big data system, he runs the keyword match based policy update query algorithm (shown in Algorithm 12) to generate a policy update query P U Q, which is submitted to the cloud platform.Suppose that the original access policy is (A, ρ) with A ∈ Z n1×n2 p and the update access policy is (A , ρ ) with A ∈ Z n 1 ×n 2 p…”

mentioning

confidence: 99%

Privacy-preserving fusion of IoT and big data for e-health

Yang

Zheng

Guo

et al. 2018

Future Generation Computer Systems

Self Cite

109

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In this paper, we propose a privacy-preserving e-health system, which is a fusion of Internet-of-things (IoT), big data and cloud storage. The medical IoT network monitors patient's physiological data, which are aggregated to electronic health record (EHR). The medical big data that contains a large amount of EHRs are outsourced to cloud platform. In the proposed system, the patient distributes an IoT group key to the medical nodes in an authenticated way without interaction round. The IoT messages are encrypted using the IoT group key and transmitted to the patient, which can be batch authenticated by the patient. The encrypted EHRs are shared among patient and different data users in a fine-grained access control manner. A novel keyword match based policy update mechanism is designed to enable flexible access policy updating without privacy leakage. Extensive comparison and simulation results demonstrate that the algorithms in the proposed system are efficient. Comprehensive analysis is provided to prove its security.

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Cross-domain dynamic anonymous authenticated group key management with symptom-matching for e-health social system

Cited by 45 publications

References 23 publications

Deep Community Detection Method for Social Networks

Deep Community Detection Method for Social Networks

A New Password- and Position-Based Authenticated Key Exchange

Privacy-preserving fusion of IoT and big data for e-health

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