Privacy preservation for the health care sector in a cloud environment by advanced hybridization mechanism

• Autorzy: Alphonsa Annie M. M. , Mohanasundaram N.
• Języki publikacji: EN

Abstrakty

EN

Cloud computing is a very popular computing model, which grants a manageable infrastructure for various kinds of functions, like storage of data, application realization and presenting, and delivery of information. The concept is therefore very dynamically advancing in all kinds of organisations, including, in particular, the health care sector. However, effective analysis and extraction of information is a challenging issue that must find adequate solutions as soon as possible, since the medical scenarios are heavily dependent on such computing aspects as data security, computing standards and compliance, governance, and so on. In order to contribute to the resolution of the issues, associated with these aspects, this paper proposes a privacy-preserving algorithm for both data sanitization and restoration processes. Even though a high number of researchers contributed to the enhancement of the restoration process, the joint sanitization and restoration process still faces some problems, such as high cost. To attain better results with a possibly low cost, this paper proposes a hybrid algorithm, referred to as GlowWorm Swarm Employed Bee (GWOSEB) for realization of both data sanitization and data restoration process. The proposed GWOSEB algorithm is compared as to its performance with some of the existing approaches, such as the conventional Glowworm Swarm Optimization (GSO), FireFly (FF), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Genetically Modified Glowworm Swarm (GMGW), in terms of analysis involving the best, worst, mean, median and standard deviation values, sanitization and restoration effectiveness, convergence analysis, and sensitivity analysis of the generated optimal key. The comparison shows the supremacy of the developed approach.

Słowa kluczowe

EN

medical data; privacy preservation; Glowworm Swarm Optimization; data hiding; data restoration

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2020
• Tom: Vol. 49, No. 4
• Strony: 499--524
• Opis fizyczny: Bibliogr. 44 poz., rys., tab.

Twórcy

autor

Alphonsa Annie M. M.

• Karpagam Academy of Higher Education, Salem - Kochi Highway, Eachanari, Coimbatore, Tamil Nadu 641021, India

autor

Mohanasundaram N.

• Karpagam Academy of Higher Education, Salem - Kochi Highway, Eachanari, Coimbatore, Tamil Nadu 641021, India

Bibliografia

• Alphonsa, M.A. and Amudhavalli, P. (2018) Genetically modified glowworm swarm optimization based privacy preservation in cloud computing for healthcare sector. Evolutionary Intelligence, 11(1-2): 101-116.
• Azadeh, A., Fam, I.M., Khoshnoud, M. and Nikafrouz, M. (2008) Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery. Information Sciences, 178(22): 4280-4300.
• Barua, M., Liang, X., Lu, R. and Shen, X. (2011) ESPAC: Enabling security and patient-centric access control for ehealth in cloud computing. International Journal of Security and Networks, 6 (2-3): 67–76.
• Bianchini, V., Cecilia, M.R., Roncone, R. and Cofini, V. (2017) Prevalence and factors associated with problematic internet use: an Italian survey among L’Aquila students. Riv. Psichiatr, 52(2): 90-93.
• Bossolasco, M. and Fenoglio, L.M. (2018) Yet another PECS usage: A continuous PECS block for anterior shoulder surgery. Journal of Anaesthesiology. Clinical pharmacology, 34(4): 569.
• Chandramohan, D., Vengattaraman, T. and Dhavachelvan, P. (2017) A secure data privacy preservation for on-demand cloud service. Journal of King Saud University-Engineering Sciences, 29(2), 144-150.
• Fister, I., Fister, I., Yang, X.-S. and Brest, J. (2013) A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13: 34-46.
• Gatzoulis, L. and Iakovidis, I. (2007) Wearable and Portable eHealth Systems. IEEE Engineering in Medicine and Biology Magazine, 26(5): 51-56.
• George, A. and Rajakumar, B.R. (2013) On Hybridizing Fuzzy Min Max Neural Network and Firefly Algorithm for Automated Heart Disease Diagnosis. In: Fourth International Conference on Computing,Communications and Networking Technologies, Tiruchengode, India (ICCCNT), 1-5. IEEE.
• Gracco, A., Luca, L., Cozzani, M. and Siciliani, G. (2007) Assessment of palatal bone thickness in adults with cone beam computerised tomography. Australian Orthodontic Journal, 23(2): 109.
• Grobauer, B., Walloschek, T. and Stocker, E. (2011) Understanding Cloud Computing Vulnerabilities. IEEE Security & Privacy, 9(2): 50-57.
• Iakovidis, I. (1998) Towards personal health record: current situation, obstacles and trends in implementation of electronic healthcare record in Europe. International Journal of Medical Informatics, 52(1-3): 105-115.
• Karaboga, D. and Basturk, B. (2008) On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1): 687-697.
• Lee, S.H., Song, J.H. and Kim, I.K. (2016) CDA Generation and Integration for Health Information Exchange Based on Cloud Computing System. IEEE Transactions on Services Computing, 9(2): 241-249.
• Li, H., Xiong, L., Ohno-Machado, L. and Jiang, X. (2014) Privacy preserving RBF kernel support vector machine. BioMed Research International. Article 827371.
• Liu, X., Lu, R., Ma, J., Chen, L and Qin, B. (2016) Privacy-Preserving Patient-Centric Clinical Decision Support System on Na¨ıve Bayesian Classification. IEEE Journal of Biomedical and Health Informatics, 20(2): 655-668.
• Lombardo, L., Scuzzo, G., Arreghini, A., Gorgun, ¨ O., Ortan, Y.¨ O. and Siciliani, G. (2014) 3D FEM comparison of lingual and labial orthodontics in en masse retraction. Progress in Orthodontics, 15(1): 38.
• Lu, R., Liang, X., Li, X., Lin, X. and Shen, X. (2012) EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications. IEEE Transactions on Parallel and Distributed Systems, 23(9): 1621-1631.
• Lu, R., Lin, X. and Shen, X. (2010) SPRING: A Social-based Privacypreserving Packet Forwarding Protocol for Vehicular Delay Tolerant Networks. In: 2010 Proceedings IEEE Infocom, 1-9.
• Manassero, A., Bossolasco, M., Ugues, S. and Bailo, C. (2014) An atypical case of two instances of mepivacaine toxicity. Journal of Anaesthesiology. Clinical Pharmacology, 30(4): 582.
• Manfredini, D., Seg`u, M., Arveda, N., Lombardo, L., Siciliani, G., Rossi, A. and Guarda-Nardini, L. (2016) Temporomandibular joint disorders in patients with different facial morphology. A systematic review of the literature. Journal of Oral and Maxillofacial Surgery, 74(1): 29-46.
• Mc Call, J. (2005) Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics, 184(1): 205-222.
• Monkaresi, H., Calvo, R.A. and Yan, H. (2014) A Machine Learning Approach to Improve Contactless Heart Rate Monitoring Using a Webcam. IEEE Journal of Biomedical and Health Informatics, 18(4): 1153-1160.
• Moreira, M.G., Ferraz, G.A.S., Barbosa, B.D.S., Iwasaki, E.M., Ferraz, P.F.P., Damasceno, F.A. and Rossi, G. (2019) Design and construction of a low-cost remotely piloted aircraft for precision agriculture applications. Agronomy Research 17(5), 1984–1992.
• Nallakumar, R., Sengottaiyan, N. and Arif, M.M. (2014) Cloud Computing and Methods for Privacy Preservation: A Survey. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 3(11).
• Sahi, A., Lai, D. and Li, Y. (2016) Security and privacy preserving approaches in the eHealth clouds with disaster recovery plan. Computers in Biology and Medicine, 78, 1-8.
• Shi, E., Chan, T., Rieffel, E., Chow, R. and Song, D. (2011) Privacypreserving aggregation of time-series data. Proc. NDSS 2, 1-17.
• Shi, J., Zhang, R., Liu, Y. and Zhang, Y. (2010) PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems. In: 2010 Proceedings IEEE INFOCOM, San Diego, CA, 1-9.
• Takabi, H., Joshi, J.B.D. and Ahn, G.J. (2010) Security and Privacy Challenges in Cloud Computing Environments. IEEE Security & Privacy, 8(6): 24-31.
• Tanweer, M.R., Suresh, S. and Sundararajan, N. (2015) Self regulating particle swarm optimization algorithm. Information Sciences, 294: 182-202.
• Viswanathan, H., Chen, B. and Pompili, D. (2012) Research challenges in computation, communication, and context awareness for ubiquitous healthcare. IEEE Communications Magazine, 50(5): 92-99.
• Wang, W., Chen, L and Zhang, Q. (2015) Outsourcing high-dimensional healthcare data to cloud with personalized privacy preservation. Computer Networks, 88, 136-148.
• Wang, X., Bai, L., Yang, Q., Wang, L. and Jiang, F. (2019) A dual privacy-preservation scheme for cloud-based eHealth systems. Journal of Information Security and Applications, 47: 132-138.
• Waqar, A., Raz, A., Abbas, H. and Khan, M.K. (2013) A framework for preservation of cloud users’ data privacy using dynamic reconstruction of metadata. Journal of Network and Computer Applications, 36, 235–248.
• Wu, B., Qian, C., Ni, W. and Fan, S. (2012) The improvement of glowworm swarm optimization for continuous optimization problems. Expert Systems with Applications, 39(7): 6335-6342.
• Zhang, X., Liu, C., Nepal, S. and Chen, I. (2013) An efficient quasiidentifier index based approach for privacy preservation over incremental data sets on cloud. Journal of Computer and System Sciences, 79(5): 542-555.
• Zhang, K., Liang, X., Baura, M., Lu, R. and Shen, X. (2014a) PHDA: A priority based health data aggregation with privacy preservation for cloud assisted WBANs. Information Sciences, 284, 130-141.
• Zhang, K., Liang, X., Shen, X. and Lu, R. (2014b). Exploiting multimedia services in mobile social networks from security and privacy perspectives. IEEE Communications Magazine, 52(3), 58-65.
• Zhang, X., Liu, C., Nepal, S. and Chen, I. (2013) An efficient quasiidentifier index based approach for privacy preservation over incremental data sets on cloud. Journal of Computer and System Sciences, 79(5): 542-555.
• Zhou, J., Cao, Z., Dong, X. and Lin, X. (2015a) PPDM: A Privacy-Preserving Protocol for Cloud-Assisted e-Healthcare Systems. IEEE Journal of Selected Topics in Signal Processing, 9(7): 1332-1344.
• Zhou, J., Cao, Z., Dong, X. and Lin, X. (2015b) TR-MABE: White-box traceable and revocable multi-authority attribute-based encryption and its applications to multi-level privacy-preserving e-healthcare cloud computing systems. In: 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 2398-2406.
• Zhou, J., Cao, Z., Dong, X., Xiong, N. and Vasilakos, A.V. (2015c) 4S: A secure and privacy-preserving key management scheme for cloudassisted wireless body area network in m-healthcare social networks. Information Sciences, 314: 255-276.
• Zhou, Y., Zhou, G., Wang, Y and Zhao, G. (2013) A Glowworm Swarm Optimization Algorithm Based Tribes. Applied Mathematics and Information Sciences, 7(2): 537-541.
• Zissis, D. and Lekkas, D. (2012) Addressing cloud computing security issues. Future Generation Computer Systems, 28(3): 583-592.