Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems
Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing pl...
- Autores:
-
Escorcia-Gutierrez, José
Mansour, Romany F.
Leal, Esmeide
Villanueva, Jair
Jiménez-Cabas, Javier
Soto, Carlos
Soto-Diaz, Roosvel
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9963
- Acceso en línea:
- https://hdl.handle.net/11323/9963
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial intelligence
Healthcare system
Energy efficiency
Blockchain
Internet of things
- Rights
- embargoedAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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| dc.title.eng.fl_str_mv |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| title |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| spellingShingle |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems Artificial intelligence Healthcare system Energy efficiency Blockchain Internet of things |
| title_short |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| title_full |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| title_fullStr |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| title_full_unstemmed |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| title_sort |
Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, José Mansour, Romany F. Leal, Esmeide Villanueva, Jair Jiménez-Cabas, Javier Soto, Carlos Soto-Diaz, Roosvel |
| dc.contributor.author.none.fl_str_mv |
Escorcia-Gutierrez, José Mansour, Romany F. Leal, Esmeide Villanueva, Jair Jiménez-Cabas, Javier Soto, Carlos Soto-Diaz, Roosvel |
| dc.subject.proposal.eng.fl_str_mv |
Artificial intelligence Healthcare system Energy efficiency Blockchain |
| topic |
Artificial intelligence Healthcare system Energy efficiency Blockchain Internet of things |
| dc.subject.proposal.fra.fl_str_mv |
Internet of things |
| description |
Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-03-15T22:15:59Z |
| dc.date.available.none.fl_str_mv |
2023-03-15T22:15:59Z 2024 |
| dc.date.issued.none.fl_str_mv |
2023 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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Escorcia-Gutierrez, J., Mansour, R.F., Leal, E. et al. Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02115-9 |
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1383-469X |
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https://hdl.handle.net/11323/9963 |
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10.1007/s11036-023-02115-9 |
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1572-8153 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Escorcia-Gutierrez, J., Mansour, R.F., Leal, E. et al. Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02115-9 1383-469X 10.1007/s11036-023-02115-9 1572-8153 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/9963 https://repositorio.cuc.edu.co/ |
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eng |
| language |
eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Mobile Networks and Applications |
| dc.relation.references.spa.fl_str_mv |
Alqaralleh BA, Vaiyapuri T, Parvathy VS, Gupta D, Khanna A, Shankar K (2021) Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things environment. Personal and Ubiquitous Computing, pp 1–11 Dwivedi AD, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):326 Abdolkhani R, Gray K, Borda A, DeSouza R (2019) Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open (ooz036). https://doi.org/10.1093/jamiaopen/ooz036 Veeramakali T, Siva R, Sivakumar B, Mahesh PS, Krishnaraj N (2021) An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. The Journal of Supercomputing 77:9576–9596 Uddin MA, Stranieri A, Gondal I, Balasubramanian V (2018) Continuous patient monitoring with a patient centric agent: a block architecture. IEEE Access 6:32700–32726 Uddin MA, Stranieri A, Gondal I, Balasubramanian (2018) A patient agent to manage blockchains for remote patient monitoring. Stud Health Technol Inform 254:105–115 Tuli S, Mahmud R, Tuli S, Buyya R (2018) Fogbus: a blockchain-based lightweight framework for edge and fog computing, arXiv: 1811.11978 Li Z-t, Chen Q, Zhu G-m, Choi Y-j, Sekiya H (2015) A low latency, energy efficient MAC protocol for wireless sensor networks. Int J Distrib Sens Netw 11(8):1–9 Wang W, Garofalakis M, Ramchandran K (2007) Distributed sparse random projection for refinable approximation, IEEE 6th International Symposium on Information Processing in Sensor Networks, pp 331–339 Ullah F, Ullah I, Khan A, Uddin MI, Alyami H, Alosaimi W (2020) Enabling clustering for privacy-aware data dissemination based on Medical Healthcare-IoTs (MH-IoTs) for wireless body area network. Journal of Healthcare Engineering 2020:1–10 Shukla S, Thakur S, Hussain S, Breslin JG, Jameel SM (2021) Identification and authentication in healthcare Internet-of-Things using integrated fog computing based blockchain model. Internet of Things 15:100422 Dwivedi AD, Malina L, Dzurenda P, Srivastava G (2019) Optimized blockchain model for internet of things based healthcare applications. In: 2019 42nd international conference on telecommunications and signal processing (TSP). IEEE, pp 135–139 Shynu PG, Menon VG, Kumar RL, Kadry S, Nam Y (2021) Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing. IEEE Access 9:45706–45720 Guo X, Lin H, Wu Y, Peng M (2020) A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Futur Gener Comput Syst 113:407–417 Honar Pajooh H, Rashid M, Alam F, Demidenko S (2021) Multi-layer blockchain-based security architecture for internet of things. Sensors 21(3):772 Kumar R, Tripathi R (2021) Towards design and implementation of security and privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs technology. The Journal of Supercomputing 77:7916–7955 Hossein KM, Esmaeili ME, Dargahi T (2019) Blockchain-based privacy-preserving healthcare architecture. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, pp 1–4 Bhattacharya P, Mehta P, Tanwar S, Obaidat MS, Hsiao KF (2020) HeaL: A blockchain-envisioned signcryption scheme for healthcare IoT ecosystems. In: 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE, pp 1–6 Alzubi JA (2021) Blockchain-based Lamport Merkle Digital signature: authentication tool in IoT healthcare. Comput Commun 170:200–208 Ray PP, Chowhan B, Kumar N, Almogren A (2021) BIoTHR: Electronic health record servicing scheme in IoT-blockchain ecosystem. IEEE Internet of Things Journal 8(13):10857–10872 Frikha T, Chaari A, Chaabane F, Cheikhrouhou O, Zaguia A (2021) Healthcare and fitness data management using the IoT-based blockchain platform. Journal of Healthcare Engineering 2021:1–12 Zaabar B, Cheikhrouhou O, Jamil F, Ammi M, Abid M (2021) HealthBlock: A secure blockchain-based healthcare data management system. Comput Netw 200:108500 Mansour RF (2022) Blockchain assisted clustering with Intrusion Detection System for Industrial Internet of Things environment. Expert Systems with Applications 207:117995 Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249 Gomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626 Nguyen TT (2021) Enhanced sunflower optimization for placement distributed generation in distribution system. Int J Electr Comput Eng 11(1):107 Suh S, Lee H, Lukowicz P, Lee YO (2021) CEGAN: classification enhancement generative adversarial networks for unraveling data imbalance problems. Neural Netw 133:69–86 Nguyen GN, Le Viet NH, Devaraj AFS, Gobi R, Shankar K (2020) Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks. Sustain Comput Inform Syst 28:100464 Dua D, and Graff C (2019) UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science Bhuvaneeswari R, Sudhakar P, Prabakaran G (2019) Heart disease prediction model based on gradient boosting tree (GBT) classification algorithm. Int J Recent Technol Eng 8(2):41–51 |
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Atribución 4.0 Internacional (CC BY 4.0)© 2023 Springer Nature Switzerland AG. Part of Springer Nature.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfEscorcia-Gutierrez, Joséc73571eac7cd701b7b53e2880a332606600Mansour, Romany F.add7b11391c69373c38bc30f68a36bf0Leal, Esmeideebfc7206259f31fe6d5bac28d2515cab600Villanueva, Jair79468dec80d5e424401aa8085e433316Jiménez-Cabas, Javier66d87f68c554f0e0264e1923d8d87067600Soto, Carlos6468e07daa5a1c3a96448d3674fc4e32Soto-Diaz, Roosvele62eb4c7db7dbdcaa12e42b2ef56e8546002023-03-15T22:15:59Z20242023-03-15T22:15:59Z2023Escorcia-Gutierrez, J., Mansour, R.F., Leal, E. et al. Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02115-91383-469Xhttps://hdl.handle.net/11323/996310.1007/s11036-023-02115-91572-8153Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods.1 páginaapplication/pdfengSpringer NetherlandsNetherlandshttps://link.springer.com/article/10.1007/s11036-023-02115-9Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systemsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceMobile Networks and ApplicationsAlqaralleh BA, Vaiyapuri T, Parvathy VS, Gupta D, Khanna A, Shankar K (2021) Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things environment. Personal and Ubiquitous Computing, pp 1–11Dwivedi AD, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):326Abdolkhani R, Gray K, Borda A, DeSouza R (2019) Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open (ooz036). https://doi.org/10.1093/jamiaopen/ooz036Veeramakali T, Siva R, Sivakumar B, Mahesh PS, Krishnaraj N (2021) An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. The Journal of Supercomputing 77:9576–9596Uddin MA, Stranieri A, Gondal I, Balasubramanian V (2018) Continuous patient monitoring with a patient centric agent: a block architecture. IEEE Access 6:32700–32726Uddin MA, Stranieri A, Gondal I, Balasubramanian (2018) A patient agent to manage blockchains for remote patient monitoring. Stud Health Technol Inform 254:105–115Tuli S, Mahmud R, Tuli S, Buyya R (2018) Fogbus: a blockchain-based lightweight framework for edge and fog computing, arXiv: 1811.11978Li Z-t, Chen Q, Zhu G-m, Choi Y-j, Sekiya H (2015) A low latency, energy efficient MAC protocol for wireless sensor networks. Int J Distrib Sens Netw 11(8):1–9Wang W, Garofalakis M, Ramchandran K (2007) Distributed sparse random projection for refinable approximation, IEEE 6th International Symposium on Information Processing in Sensor Networks, pp 331–339Ullah F, Ullah I, Khan A, Uddin MI, Alyami H, Alosaimi W (2020) Enabling clustering for privacy-aware data dissemination based on Medical Healthcare-IoTs (MH-IoTs) for wireless body area network. Journal of Healthcare Engineering 2020:1–10Shukla S, Thakur S, Hussain S, Breslin JG, Jameel SM (2021) Identification and authentication in healthcare Internet-of-Things using integrated fog computing based blockchain model. Internet of Things 15:100422Dwivedi AD, Malina L, Dzurenda P, Srivastava G (2019) Optimized blockchain model for internet of things based healthcare applications. In: 2019 42nd international conference on telecommunications and signal processing (TSP). IEEE, pp 135–139Shynu PG, Menon VG, Kumar RL, Kadry S, Nam Y (2021) Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing. IEEE Access 9:45706–45720Guo X, Lin H, Wu Y, Peng M (2020) A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Futur Gener Comput Syst 113:407–417Honar Pajooh H, Rashid M, Alam F, Demidenko S (2021) Multi-layer blockchain-based security architecture for internet of things. Sensors 21(3):772Kumar R, Tripathi R (2021) Towards design and implementation of security and privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs technology. The Journal of Supercomputing 77:7916–7955Hossein KM, Esmaeili ME, Dargahi T (2019) Blockchain-based privacy-preserving healthcare architecture. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, pp 1–4Bhattacharya P, Mehta P, Tanwar S, Obaidat MS, Hsiao KF (2020) HeaL: A blockchain-envisioned signcryption scheme for healthcare IoT ecosystems. In: 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE, pp 1–6Alzubi JA (2021) Blockchain-based Lamport Merkle Digital signature: authentication tool in IoT healthcare. Comput Commun 170:200–208Ray PP, Chowhan B, Kumar N, Almogren A (2021) BIoTHR: Electronic health record servicing scheme in IoT-blockchain ecosystem. IEEE Internet of Things Journal 8(13):10857–10872Frikha T, Chaari A, Chaabane F, Cheikhrouhou O, Zaguia A (2021) Healthcare and fitness data management using the IoT-based blockchain platform. Journal of Healthcare Engineering 2021:1–12Zaabar B, Cheikhrouhou O, Jamil F, Ammi M, Abid M (2021) HealthBlock: A secure blockchain-based healthcare data management system. Comput Netw 200:108500Mansour RF (2022) Blockchain assisted clustering with Intrusion Detection System for Industrial Internet of Things environment. Expert Systems with Applications 207:117995Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249Gomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626Nguyen TT (2021) Enhanced sunflower optimization for placement distributed generation in distribution system. Int J Electr Comput Eng 11(1):107Suh S, Lee H, Lukowicz P, Lee YO (2021) CEGAN: classification enhancement generative adversarial networks for unraveling data imbalance problems. Neural Netw 133:69–86Nguyen GN, Le Viet NH, Devaraj AFS, Gobi R, Shankar K (2020) Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks. Sustain Comput Inform Syst 28:100464Dua D, and Graff C (2019) UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer ScienceBhuvaneeswari R, Sudhakar P, Prabakaran G (2019) Heart disease prediction model based on gradient boosting tree (GBT) classification algorithm. 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corporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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