Hybrid privacy-preserving clinical decision support system in fog–cloud computing

Future Generation Computer Systems

Volumn 78, Issue , 2018, Pages 825-837

  Liu, Ximeng ^a,b   Deng, Robert H ^b   Yang, Yang ^a,b   Tran, Hieu N ^b   Zhong, Shangping ^a  

^a FUZHOU UNIVERSITY   (China)

^b SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

Author keywords

Clinical decision support system; Cloud computing; Fog computing; Neural networks; Privacy preserving

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLOUD COMPUTING; DATA MINING; DATA PRIVACY; DECISION SUPPORT SYSTEMS; FOG; INTERNET PROTOCOLS; NETWORK FUNCTION VIRTUALIZATION; NETWORK LAYERS; NETWORK SECURITY; NEURAL NETWORKS; PATIENT MONITORING; PRODUCT DESIGN;

ACCURATE PREDICTION; ACTIVATION FUNCTIONS; CALCULATION PROTOCOLS; CLINICAL DECISION SUPPORT SYSTEMS; DATA MINING METHODS; HEALTH CONDITION; PIECEWISE POLYNOMIALS; PRIVACY PRESERVING;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 85016064811     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.03.018     Document Type: Article

References (46)

1. Kortuem, G., Kawsar, F., Fitton, D., Sundramoorthy, V., Smart objects as building blocks for the Internet of things. IEEE Internet Comput. 14:1 (2010), 44–51.
2. Atzori, L., Iera, A., Morabito, G., The Internet of things: A survey. Comput. Netw. 54:15 (2010), 2787–2805.
3. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. A view of cloud computing. Commun. ACM 53:4 (2010), 50–58.
4. Li, M., Yu, S., Zheng, Y., Ren, K., Lou, W., Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Trans. Parallel Distrib. Syst. 24:1 (2013), 131–143.
5. Bonomi, F., Milito, R., Zhu, J., Addepalli, S., Fog computing and its role in the Internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 2012, ACM, 13–16.
6. Bonomi, F., Milito, R., Natarajan, P., Zhu, J., Fog computing: A platform for Internet of things and analytics. Big Data and Internet of Things: A Roadmap for Smart Environments, 2014, Springer, 169–186.
7. Bieber, G., Haescher, M., Vahl, M., Sensor requirements for activity recognition on smart watches. Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments, 2013, ACM, 67.
8. Ballerstädt, R., Ehwald, R., Suitability of aqueous dispersions of dextran and concanavalin a for glucose sensing in different variants of the affinity sensor. Biosens. Bioelectron. 9:8 (1994), 557–567.
9. Ganesan, D., Greenstein, B., Perelyubskiy, D., Estrin, D., Heidemann, J., An evaluation of multi-resolution storage for sensor networks. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, 2003, ACM, 89–102.
10. Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators. Neural Netw. 2:5 (1989), 359–366.
11. Lisboa, P.J., A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 15:1 (2002), 11–39.
12. Kong, G., Xu, D.-L., Yang, J.-B., Clinical decision support systems: a review on knowledge representation and inference under uncertainties. Int. J. Comput. Intell. Syst. 1:2 (2008), 159–167.
13. Casola, V., Castiglione, A., Choo, K.R., Esposito, C., Healthcare-related data in the cloud: Challenges and opportunities. IEEE Cloud Comput. 3:6 (2016), 10–14.
14. Nepal, S., Ranjan, R., Choo, K.R., Trustworthy processing of healthcare big data in hybrid clouds. IEEE Cloud Comput. 2:2 (2015), 78–84.
15. Liu, X., Choo, K.-K.R., Deng, R.H., Lu, R., Weng, J., Efficient and privacy-preserving outsourced computation of rational numbers. IEEE Trans. Dependable Secure Comput., 2016.
16. Paillier, P., Public-key cryptosystems based on composite degree residuosity classes. Advances in Cryptology, EUROCRYPT99, 1999, Springer, 223–238.
17. Liu, X., Deng, R.H., Choo, K.-K.R., Weng, J., An efficient privacy-preserving outsourced calculation toolkit with multiple keys. IEEE Trans. Inf. Forensics Secur. 11:11 (2016), 2401–2414.
18. S. Kamara, P. Mohassel, M. Raykova, Outsourcing multi-party computation, IACR Cryptology ePrint Archive 2011 (2011) 272. URL http://eprint.iacr.org/2011/272.
19. Peter, A., Tews, E., Katzenbeisser, S., Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8:12 (2013), 2046–2058.
20. M. Lichman, UCI machine learning repository, 2013. URL http://archive.ics.uci.edu/ml.
21. Knuth, D.E., Seminumerical Algorithm (Arithmetic) the Art of Computer Programming, Vol. 2. 1981.
22. Rahulamathavan, Y., Veluru, S., Phan, R.C.-W., Chambers, J.A., Rajarajan, M., Privacy-preserving clinical decision support system using gaussian kernel-based classification. IEEE J. Biomed. Health Inform. 18:1 (2014), 56–66.
23. Liu, X., Lu, R., Ma, J., Chen, L., Qin, B., Privacy-preserving patient-centric clinical decision support system on Naive Bayesian classification. IEEE J. Biomed. Health Inform. 20 (2016), 655–668.
24. Secretan, J., Georgiopoulos, M., Castro, J., A privacy preserving probabilistic neural network for horizontally partitioned databases. International Joint Conference on Neural Networks, 2007, IEEE, IJCNN 2007, 1554–1559.
25. Chen, T., Zhong, S., Privacy-preserving backpropagation neural network learning. IEEE Trans. Neural Netw. 20:10 (2009), 1554–1564.
26. Yuan, J., Yu, S., Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans. Parallel Distrib. Syst. 25:1 (2014), 212–221.
27. A.C. Yao, How to generate and exchange secrets (extended abstract), in: 27th Annual Symposium on Foundations of Computer Science, Toronto, Canada, 27–29 October 1986, 1986, pp. 162–167.
28. Y. Huang, D. Evans, J. Katz, L. Malka, Faster secure two-party computation using garbled circuits, in: 20th USENIX Security Symposium, San Francisco, CA, USA, August 8–12, 2011, Proceedings, 2011.
29. Bellare, M., Hoang, V.T., Rogaway, P., Foundations of garbled circuits. Proceedings of the 2012 ACM Conference on Computer and Communications Security, 2012, ACM, 784–796.
30. Bogdanov, D., Laur, S., Willemson, J., Sharemind: A framework for fast privacy-preserving computations. Computer Security, ESORICS 2008, 2008, Springer, 192–206.
31. Keller, M., Scholl, P., Smart, N.P., An architecture for practical actively secure mpc with dishonest majority. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, 2013, ACM, 549–560.
32. Brickell, J., Porter, D.E., Shmatikov, V., Witchel, E., Privacy-preserving remote diagnostics. Proceedings of the 14th ACM Conference on Computer and Communications Security, 2007, ACM, 498–507.
33. Barni, M., Failla, P., Kolesnikov, V., Lazzeretti, R., Sadeghi, A.-R., Schneider, T., Secure evaluation of private linear branching programs with medical applications. Computer Security, ESORICS 2009, 2009, Springer, 424–439.
34. Katz, J., Ostrovsky, R., Round-optimal secure two-party computation. Advances in Cryptology, CRYPTO 2004, 2004, Springer, 335–354.
35. Vaidya, J., Clifton, C., Privacy preserving association rule mining in vertically partitioned data. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, ACM, 639–644.
36. Kantarcioglu, M., Clifton, C., Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. Knowl. Data Eng.(9), 2004, 1026–1037.
37. Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J., Privacy preserving mining of association rules. Inf. Syst. 29:4 (2004), 343–364.
38. Bansal, A., Chen, T., Zhong, S., Privacy preserving back-propagation neural network learning over arbitrarily partitioned data. Neural Comput. Appl. 20:1 (2011), 143–150.
39. Vaidya, J., Clifton, C., Privacy-preserving k-means clustering over vertically partitioned data. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, ACM, 206–215.
40. Jagannathan, G., Wright, R.N., Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005, ACM, 593–599.
41. Gentry, C., et al. Fully homomorphic encryption using ideal lattices. STOC, Vol. 9, 2009, 169–178.
42. Smart, N.P., Vercauteren, F., Fully homomorphic encryption with relatively small key and ciphertext sizes. Public Key Cryptography, PKC 2010, 2010, Springer, 420–443.
43. L. Morris, Analysis of partially and fully homomorphic encryption, http://www.liammorris.com/crypto2/Homomorphic%20Encryption%20Paper.pdf (2013).
44. ElGamal, T., A public key cryptosystem and a signature scheme based on discrete logarithms. Advances in Cryptology, 1985, Springer, 10–18.
45. Brakerski, Z., Vaikuntanathan, V., Efficient fully homomorphic encryption from (standard) lwe. SIAM J. Comput. 43:2 (2014), 831–871.
46. E. Ayday, J.L. Raisaro, M. Laren, P. Jack, J. Fellay, J.-P. Hubaux, Privacy-preserving computation of disease risk by using genomic, clinical, and environmental data, in: Proceedings of USENIX Security Workshop on Health Information Technologies, HealthTech”13, no. EPFL-CONF-187118, 2013.