Proceedings of the 2nd International Conference on Big Data, Cloud and Applications 2017
DOI: 10.1145/3090354.3090388
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Secure confidential big data sharing in cloud computing using KP-ABE

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Cited by 4 publications
(5 citation statements)
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“…A message is encrypted with a set of attributes in the KP-ABE scheme, and the user's private keys are linked to an access structure. If the access structure in the user's private key can satisfy the encrypted data's attributes, the message can be recovered successfully [2,4,[10][11][12]16,17,[30][31][32][33][34][35][36][37][38][39][40]. Generally, the KP-ABE scheme is constructed based on four basic algorithms: setup, key generation, encryption, and decryption, as summarized below.…”
Section: Kp-abe Schemesmentioning
confidence: 99%
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“…A message is encrypted with a set of attributes in the KP-ABE scheme, and the user's private keys are linked to an access structure. If the access structure in the user's private key can satisfy the encrypted data's attributes, the message can be recovered successfully [2,4,[10][11][12]16,17,[30][31][32][33][34][35][36][37][38][39][40]. Generally, the KP-ABE scheme is constructed based on four basic algorithms: setup, key generation, encryption, and decryption, as summarized below.…”
Section: Kp-abe Schemesmentioning
confidence: 99%
“…While ABE promises a solution to address the scalability issues of recent public key infrastructure (PKI), how the quantum-resistant ABE schemes can be further extended and cope well to support the real-world implementation of big data analytics with its characteristics of high volume, high velocity and high velocity, have attracted numerous researches proposals [32,[69][70][71][72][73][74][75][76][77]. The ABE scheme is a suitable access control method that secures the private and sensitive big data stored in the cloud.…”
Section: Ongoing Challenges and Future Suggestionsmentioning
confidence: 99%
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“…una revisión de literatura relevante.-Se ha categorizado las arquitecturas de acuerdo a los objetivos encontrados en otros trabajos relevantes, y las categorías son: prevención de enfermedades [5], [33], [35], [36] y [37] es decir, el 25% se concentraron en obtener mejores prácticas de salud o alimentación; causas de reclamos [7] y [38] es decir, el 10% se concentraron en las principales causas o razones que hacen los clientes en sus reclamos; expresión de sentimientos [14] es decir, el 5% se concentró en conocer las opiniones positivas o negativas sobre los reclamos; fraudes en reclamos [11], [31], [32], [34], [39], [40], [41], [42] es decir, el 40% de las arquitecturas se concentraron en detectar fraudes o estafas en los reclamos a los seguros de salud; predicción de enfermedades [30] y [43] es decir, 10% se concentra en conocer qué enfermedades podrían acaecer sobre la población; seguridad de datos [44] y [45] es decir, 10% trabaja sobre seguridad de información sobre salud de los clientes, ver figura II.…”
Section: Categorizar Diferentes Arquitecturas Computacionales Para Se...unclassified
“…Besides, some of the researches based on CP-ABE scheme have presented to accomplish access control for Big Data [38][39] [40]. Sara et al [41] proposed an innovative hybrid approach which improves the privacy and security of big data which is shared in a cloud with the use of a method of access control based on the Key-Policy Attribute-Based Encryption (KP-ABE) and authentication system, the ciphered data might be accessed only by legitimate users. Furthermore, for the solution of problems of security and privacy which limit big data improvement, blockchainbased access control for improving big data platforms security [42][43] [44] has been proposed.…”
Section: A Access Controlmentioning
confidence: 99%