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Web-based practical privacy-preserving distributed image storage for financial services in cloud computing

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Abstract

With the development of multimedia technology and applications in the financial services industry, a large amount of multimedia image data related to financial services has been generated. For local users with limited resources including enterprises and individuals, it is a better choice to make full use of the rich storage capacity of the cloud. However, due to outsourcing the financial services image data to store on the cloud server, local users lose their direct control ability, which threatens the privacy of users. Although data security storage has attracted great attention from researchers, the protection of massive amounts of data, especially for financial services images, is brought to a new level with the advent of the era of artificial intelligence. In this paper, we focus on this issue and propose a lightweight data storage approach for privacy-preserving financial services images in the cloud. First, we use the EfficientDet neural network model to identify a large number of financial services images with sensitive data and classify the images. Then we perform different operations on the two types of images based on our proposed privacy-preserving financial services image storage architecture. Specifically, for privacy-sensitive financial services images, we use the privacy-preserving scheme we proposed to store the privacy parts on different servers, and directly outsource the storage of the remaining images without sensitive information. The experimental results show that our scheme achieves \(2x-5x\) lower than some popular image encryption schemes on time consumption, and simultaneously protects the security of images. In particular, our scheme shows more excellent performance when it comes to storing a large number of financial services multimedia images.

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References

  1. Board, F.S.: Artificial intelligence and machine learning in financial services: Market developments and financial stability implications. Financial Stability Board 45 (2017)

  2. Tan, L., Yu, K., Ming, F., Cheng, X., Srivastava, G.: Secure and resilient artificial intelligence of things: A honeynet approach for threat detection and situational awareness. IEEE Consum. Electron. Mag. 11(3), 69–78 (2022). https://doi.org/10.1109/MCE.2021.3081874

    Article  Google Scholar 

  3. Yu, K., Tan, L., Mumtaz, S., Al-Rubaye, S., Al-Dulaimi, A., Bashir, A.K., Khan, F.A.: Securing critical infrastructures: Deep-learning-based threat detection in iiot. IEEE Commun. Mag. 59(10), 76–82 (2021). https://doi.org/10.1109/MCOM.101.2001126

    Article  Google Scholar 

  4. Bagaeen, A., Al-Zoubi, S., Al-Sayyed, R., Rodan, A.: Storage as a service (staas) security challenges and solutions in cloud computing environment: An evaluation review. In: 2019 Sixth HCT Information Technology Trends (ITT), pp. 208–213. IEEE (2019)

  5. Marinescu, D.C.: Cloud Computing: Theory and Practice. Morgan Kaufmann, (2017)

  6. Mason, R.S., Rodriguez, A.: Method and system for interfacing to cloud storage. Google Patents. US Patent 8,880,474 (2014)

  7. Chacko, P.: Distributed virtual storage cloud architecture and a method thereof. Google Patents. US Patent 9,128,626 (2015)

  8. Obrutsky, S.: Cloud storage: Advantages, disadvantages and enterprise solutions for business. In: Proceedings of the Eastern Institute of Technology Conference, p. 10 (2016)

  9. Yu, J., Ren, K., Wang, C.: Enabling cloud storage auditing with verifiable outsourcing of key updates. IEEE Trans. Inf. Forensics Secur. 11(6), 1362–1375 (2016)

    Article  Google Scholar 

  10. Wang, Q., Zeng, W., Tian, J.: A compressive sensing based secure watermark detection and privacy preserving storage framework. IEEE Trans. Image Process. 23(3), 1317–1328 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Marwan, M., Kartit, A., Ouahmane, H.: A framework to secure medical image storage in cloud computing environment. J. Electron. Commer. Organ. (JECO) 16(1), 1–16 (2018)

    Article  Google Scholar 

  12. Ferreira, B., Rodrigues, J., Leitao, J., Domingos, H.: Practical privacy-preserving content-based retrieval in cloud image repositories. IEEE Trans. Cloud Comput. 7(3), 784–798 (2017)

    Article  Google Scholar 

  13. Vengadapurvaja, A., Nisha, G., Aarthy, R., Sasikaladevi, N.: An efficient homomorphic medical image encryption algorithm for cloud storage security. Procedia Comput. Sci. 115, 643–650 (2017)

    Article  Google Scholar 

  14. Feng, C., Yu, K., Aloqaily, M., Alazab, M., Lv, Z., Mumtaz, S.: Attribute-based encryption with parallel outsourced decryption for edge intelligent iov. IEEE Trans. Veh. Technol. 69(11), 13784–13795 (2020)

    Article  Google Scholar 

  15. Yang, L., Yu, K., Yang, S.X., Chakraborty, C., Lu, Y., Guo, T.: An intelligent trust cloud management method for secure clustering in 5g enabled internet of medical things. IEEE Transactions on Industrial Informatics, 1–1 (2021). https://doi.org/10.1109/TII.2021.3128954

  16. Leon, P., Ur, B., Shay, R., Wang, Y., Balebako, R., Cranor, L.: Why johnny can’t opt out: a usability evaluation of tools to limit online behavioral advertising. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 589–598 (2012)

  17. Malheiros, M., Jennett, C., Patel, S., Brostoff, S., Sasse, M.A.: Too close for comfort: A study of the effectiveness and acceptability of rich-media personalized advertising. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 579–588 (2012)

  18. Zhang, Y., Ma, X., Zhang, J., Hossain, M.S., Muhammad, G., Amin, S.U.: Edge intelligence in the cognitive internet of things: Improving sensitivity and interactivity. IEEE Network 33(3), 58–64 (2019)

    Article  Google Scholar 

  19. Lu, H., Zhang, Y., Li, Y., Jiang, C., Abbas, H.: User-oriented virtual mobile network resource management for vehicle communications. IEEE transactions on intelligent transportation systems (2020)

  20. Hua, Z., Jin, F., Xu, B., Huang, H.: 2d logistic-sine-coupling map for image encryption. Signal Process. 149, 148–161 (2018)

    Article  Google Scholar 

  21. Wang, X., Feng, L., Zhao, H.: Fast image encryption algorithm based on parallel computing system. Inf. Sci. 486, 340–358 (2019)

    Article  MATH  Google Scholar 

  22. Hua, Z., Zhou, Y., Huang, H.: Cosine-transform-based chaotic system for image encryption. Inf. Sci. 480, 403–419 (2019)

    Article  Google Scholar 

  23. Yu, J., Zhang, B., Kuang, Z., Lin, D., Fan, J.: iprivacy: Image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans. Inf. Forensics Secur. 12(5), 1005–1016 (2017). https://doi.org/10.1109/TIFS.2016.2636090

    Article  Google Scholar 

  24. Sun, Y., Liu, J., Yu, K., Alazab, M., Lin, K.: Pmrss: Privacy-preserving medical record searching scheme for intelligent diagnosis in iot healthcare. IEEE Trans. Industr. Inform. (2021)

  25. Sun, Y., Cui, Y., Huang, Y., Lin, Z.: Sdmp: A secure detector for epidemic disease file based on dnn. Inf. Fusion 68, 1–7 (2021)

    Article  Google Scholar 

  26. Tan, L., Yu, K., Shi, N., Yang, C., Wei, W., Lu, H.: Towards secure and privacy-preserving data sharing for covid-19 medical records: A blockchain-empowered approach. IEEE Trans. Netw. Sci. Eng. (2021)

  27. Han, H., Fang, L., Lu, W., Zhai, W., Li, Y., Zhao, J.: A gcica grant-free random access scheme for m2m communications in crowded massive mimo systems. IEEE Internet Things J. 9(8), 6032–6046 (2022). https://doi.org/10.1109/JIOT.2021.3110793

    Article  Google Scholar 

  28. Xu, D., Yu, K., Ritcey, J.A.: Cross-layer device authentication with quantum encryption for 5g enabled iiot in industry 4.0. IEEE Trans. Industr. Inform, 1–1 (2021). https://doi.org/10.1109/TII.2021.3130163

  29. Sookhak, M.: Dynamic remote data auditing for securing big data storage in cloud computing. PhD thesis, University of Malaya (2015)

  30. Kartit, Z., El Marraki, M.: Applying encryption algorithm to enhance data security in cloud storage. Eng Lett 23(4) (2015)

  31. Gai, K., Qiu, M., Thuraisingham, B., Tao, L.: Proactive attribute-based secure data schema for mobile cloud in financial industry. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 1332–1337. IEEE (2015)

  32. Qiu, M., Gai, K., Thuraisingham, B., Tao, L., Zhao, H.: Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry. Future Gener. Comput. Syst. 80, 421–429 (2018)

    Article  Google Scholar 

  33. Gai, K., Qiu, L., Chen, M., Zhao, H., Qiu, M.: Sa-east: security-aware efficient data transmission for its in mobile heterogeneous cloud computing. ACM Trans. Embed. Comput. Syst. (TECS) 16(2), 1–22 (2017)

    Article  Google Scholar 

  34. Liu, S., Qu, Q., Chen, L., Ni, L.M.: Smc: A practical schema for privacy-preserved data sharing over distributed data streams. IEEE Trans. Big Data 1(2), 68–81 (2015)

    Article  Google Scholar 

  35. Zhang, L., Jung, T., Liu, C., Ding, X., Li, X.-Y., Liu, Y.: Pop: Privacy-preserving outsourced photo sharing and searching for mobile devices. In: 2015 IEEE 35th International Conference on Distributed Computing Systems, pp. 308–317. IEEE (2015)

  36. Ferreira, B., Rodrigues, J., Leitao, J., Domingos, H.: Privacy-preserving content-based image retrieval in the cloud. In: 2015 IEEE 34th Symposium on Reliable Distributed Systems (SRDS), pp. 11–20. IEEE (2015)

  37. Bohli, J.-M., Gruschka, N., Jensen, M., Iacono, L.L., Marnau, N.: Security and privacy-enhancing multicloud architectures. IEEE Trans Dependable Secure Comput 10(4), 212–224 (2013)

    Article  Google Scholar 

  38. Khan, B.U.I., Baba, A.M., Olanrewaju, R.F., Lone, S.A., Zulkurnain, N.F.: Ssm: Secure-split-merge data distribution in cloud infrastructure. In: 2015 IEEE Conference on Open Systems (ICOS), pp. 40–45. IEEE (2015)

  39. Olanrewaju, R.F., Khan, B.U.I., Baba, A., Mir, R.N., Lone, S.A.: Rfda: Reliable framework for data administration based on split-merge policy. In: 2016 SAI Computing Conference (SAI), pp. 545–552. IEEE (2016)

  40. Li, Y., Gai, K., Qiu, L., Qiu, M., Zhao, H.: Intelligent cryptography approach for secure distributed big data storage in cloud computing. Inf Sci 387, 103–115 (2017)

    Article  MATH  Google Scholar 

  41. Rawal, B.S., Vijayakumar, V., Manogaran, G., Varatharajan, R., Chilamkurti, N.: Secure disintegration protocol for privacy preserving cloud storage. Wirel. Pers. Commun 103(2), 1161–1177 (2018)

    Article  Google Scholar 

  42. Zibouh, O., Dalli, A., Drissi, H.: Cloud computing security through parallelizing fully homomorphic encryption applied to multi-cloud approach. J. Theor. Appl. Inf. Technol. 87(2), 300 (2016)

    Google Scholar 

  43. Subramanian, K., John, F.L.: Secure and reliable unstructured data sharing in multi-cloud storage using the hybrid crypto system. IJCSNS 17(6), 196–206 (2017)

    Google Scholar 

  44. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2015)

  45. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

  46. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

  47. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer (2016)

  48. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)

  49. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

  50. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

  51. Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018)

  52. Zhao, Q., Sheng, T., Wang, Y., Tang, Z., Chen, Y., Cai, L., Ling, H.: M2det: A single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9259–9266 (2019)

  53. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)

  54. Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

  55. Habutsu, T., Nishio, Y., Sasase, I., Mori, S.: A secret key cryptosystem by iterating a chaotic map. In: Workshop on the Theory and Application of of Cryptographic Techniques, pp. 127–140. Springer (1991)

  56. Pak, C., Huang, L.: A new color image encryption using combination of the 1d chaotic map. Signal Process 138, 129–137 (2017)

    Article  Google Scholar 

  57. Kulsoom, A., Xiao, D., Abbas, S.A., et al.: An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and dna complementary rules. Multimed. Tools. Appl. 75(1), 1–23 (2016)

    Article  Google Scholar 

  58. Liu, W., Sun, K., Zhu, C.: A fast image encryption algorithm based on chaotic map. Opt. Lasers Eng. 84, 26–36 (2016)

    Article  Google Scholar 

  59. Kanso, A., Ghebleh, M.: A novel image encryption algorithm based on a 3d chaotic map. Commun. Nonlinear Sci. Numer. Simul. 17(7), 2943–2959 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  60. Hua, Z., Zhou, Y., Pun, C.-M., Chen, C.P.: Image encryption using 2d logistic-sine chaotic map. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3229–3234. IEEE (2014)

  61. Joshi, A.B., Kumar, D., Gaffar, A., Mishra, D.: Triple color image encryption based on 2d multiple parameter fractional discrete fourier transform and 3d arnold transform. Opt. Lasers Eng. 133, 106139 (2020)

  62. Luo, Y., Yu, J., Lai, W., Liu, L.: A novel chaotic image encryption algorithm based on improved baker map and logistic map. Multimed. Tools. Appl. 78(15), 22023–22043 (2019)

    Article  Google Scholar 

  63. Chen, R., Akkus, I.E., Francis, P.: Splitx: High-performance private analytics. ACM SIGCOMM Comput. Commun. Rev. 43(4), 315–326 (2013)

    Article  Google Scholar 

  64. Yang, S., Luo, P., Loy, C.-C., Tang, X.: Wider face: A face detection benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533 (2016)

  65. Xu, Z., Yang, W., Meng, A., Lu, N., Huang, H., Ying, C., Huang, L.: Towards end-to-end license plate detection and recognition: A large dataset and baseline. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 255–271 (2018)

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Authors and Affiliations

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, NO 10 Xitucheng Road, Haidian District, 100876, Beijing, Beijing, China

    Cai Xiaohong, Sun Yi & Lin Zhaowen

  2. College of Applied Computer Science, King Saud University, 11692, Riyadh, Saudi Arabia

    Muhammad Imran

  3. Graduate School of Science and Engineering, Hosei University, Tokyo, 184-8584, Japan

    Yu Keping

  4. RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan

    Yu Keping

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  1. Cai Xiaohong
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This article belongs to the Topical Collection: Special Issue on Web-based Intelligent Financial Services.

Guest Editors: Hong-Ning Dai, Xiaohui Haoran, and Miguel Martinez

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Xiaohong, C., Yi, S., Zhaowen, L. et al. Web-based practical privacy-preserving distributed image storage for financial services in cloud computing. World Wide Web 26, 1223–1241 (2023). https://doi.org/10.1007/s11280-022-01090-7

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