Privacy-preserving edge-assisted image retrieval and classification in IoT

Abstract

Internet of Things (IoT) has drawn much attention in recent years. However, the image data captured by IoT terminal devices are closely related to users’ personal information, which are sensitive and should be protected. Though traditional privacy-preserving outsourced computing solutions such as homomorphic cryptographic primitives can support privacy-preserving computing, they consume a significant amount of computation and storage resources. Thus, it becomes a heavy burden on IoT terminal devices with limited resources. In order to reduce the resource consumption of terminal device, we propose an edge-assisted privacy-preserving outsourced computing framework for image processing, including image retrieval and classification. The edge nodes cooperate with the terminal device to protect data and support privacy-preserving computing on the semitrusted cloud server. Under this framework, edge-assisted privacy-preserving image retrieval and classification schemes are proposed in this paper. The security analysis and performance evaluation show that the proposed schemes greatly reduce the computational, communication and storage burden of IoT terminal device while ensuring image data security.

Keywords

Internet of Things / outsourced computation / privacy protection / cryptographic primitive / image processing

References

[1] Liu B, Li Y, Zeng B, Lei C. An efficient trust negotiation strategy towards the resource-limited mobile commerce environment. Frontiers of Computer Science, 2016, 10(3): 543–558

[2] Chen X, Huang X, Li J, Ma J, Lou W, Wong D. New algorithms for secure outsourcing of large-scale systems of linear equations. IEEE Transactions on Information Forensics and Security, 2015, 10(1): 69–78

[3] Shen J, Wang C, Li T, Chen X, Huang X, Zhan Z H. Secure data uploading scheme for a smart home system. Information Sciences, 2018, 453: 186–197

[4] Xiong J, Ren J, Chen L, Yao Z, Lin M, Wu D, Niu B. Enhancing privacy and availability for data clustering in intelligent electrical service of IoT. IEEE Internet of Things Journal, 2018, 6(2): 1530–1540

[5] Wu D, Si S, Wu S, Wang R. Dynamic trust relationships aware data privacy protection in mobile crowd-sensing. IEEE Internet of Things Journal, 2018, 5(4): 2958–2970

[6] Peng S, Yang A, Cao L, Yu S, Xie D. Social influence modeling using information theory in mobile social networks. Information Sciences, 2017, 379: 146–159

[7] Cai Z, Yan H, Li P, Huang Z A, Gao C. Towards secure and flexible EHR sharing in mobile health cloud under static assumptions. Cluster Computing, 2017, 20(3): 2415–2422

[8] Wang H, Zheng Z, Wu L, Li P. New directly revocable attribute-based encryption scheme and its application in cloud storage environment. Cluster Computing, 2017, 20(3): 2385–2392

[9] Satyanarayanan M, Simoens P, Xiao Y, Pillai P, Chen Z, Ha K, Hu W L, Amos B. Edge analytics in the Internet of Things. IEEE Pervasive Computing, 2015, 14(2): 24–31

[10] Li J, Huang X, Li J, Chen X, Xiang Y. Securely outsourcing attributebased encryption with checkability. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(8): 2201–2210

[11] Li J, Li J, Chen X, Jia C, Lou W. Identity-based encryption with outsourced revocation in cloud computing. IEEE Transactions on Computers, 2015, 64(2): 425–437

[12] Li H, Liu D, Dai Y, Luan T. Engineering searchable encryption of mobile cloud networks: when QoE meets QoP. IEEE Wireless Communications, 2015, 22(4): 74–80

[13] Xiong J, Zhang Y, Li X, Lin M, Yao Z, Liu G. RSE-PoW: a role symmetric encryption PoW scheme with authorized deduplication for multimedia data. Mobile Networks and Applications, 2018, 23(3): 650–663

[14] Li X, Li J, Huang F. A secure cloud storage system supporting privacypreserving fuzzy deduplication. Soft Computing, 2016, 20(4): 1437–1448

[15] Li J, Li J, Xie D, Cai Z. Secure auditing and deduplicating data in cloud. IEEE Transactions on Computers, 2016, 65(8): 2386–2396

[16] Gao C, Cheng Q, He P, Susilo W, Li J. Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Information Sciences, 2018, 444: 72–88

[17] Li T, Li J, Liu Z, Li P, Jia C. Differentially private Naive Bayes learning over multiple data sources. Information Sciences, 2018, 444: 89–104

[18] Li P, Li J, Huang Z, Li T, Gao C, Yiu S, Chen K. Multi-key privacypreserving deep learning in cloud computing. Future Generation Computer Systems, 2017, 74: 76–85

[19] Seo S H, Nabeel M, Ding X, Bertino E. An efficient certificateless encryption for secure data sharing in public clouds. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(9): 2107–2119

[20] Ali M, Dhamotharan R, Khan E, Khan S U, Vasilakos A V, Li K, Zomaya A Y. SeDaSC: secure data sharing in clouds. IEEE Systems Journal, 2017, 11(2): 395–404

[21] Wu Z, Tian L, Li P, Wu T, Jiang M, Wu C. Generating stable biometric keys for flexible cloud computing authentication using finger vein. Information Sciences, 2018, 433: 431–447

[22] Zhang L, Jung T, Liu K, Li X, Ding X, Gu J, Liu Y. PIC: enable largescale privacy preserving content-based image search on cloud. IEEE Transactions on Parallel and Distributed Systems, 2017, 28(11): 3258–3271

[23] Li J, Chen X, Li M, Li J, Lee P P C, Lou W. Secure deduplication with efficient and reliable convergent key management. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(6): 1615–1625

[24] Chen X, Li J, Huang X, Ma J, Lou W. New publicly verifiable databases with efficient updates. IEEE Transactions on Dependable and Secure Computing, 2015, 12(5): 546–556

[25] Chen X, Li J, Weng J, Ma J, Lou W. Verifiable computation over large database with incremental updates. IEEE Transactions on Computers, 2016, 65(10): 3184–3195

[26] Hahn F, Kerschbaum F. Searchable encryption with secure and efficient updates. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. 2014, 310–320

[27] Kuzu M, Islam M S, Kantarcioglu M. Efficient similarity search over encrypted data. In: Proceedings of the 28th IEEE International Conference on Data Engineering. 2012, 1156–1167

[28] Gao C, Lv S, Wei Y, Wang Z, Liu Z, Cheng X. M-SSE: an effective searchable symmetric encryption with enhanced security for mobile devices. IEEE Access, 2018, 6: 38860–38869

[29] Cheng B, Zhuo L, Bai Y, Peng Y, Zhang J. Secure index construction for privacy-preserving large-scale image retrieval. In: Proceedings of the 4th IEEE International Conference on Big Data and Cloud Computing. 2014, 116–120

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

[31] Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K. A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Transactions on Information Forensics and Security, 2016, 11(11): 2594–2608

[32] Cheng H, Zhang X, Yu J, Li F. Markov process-based retrieval for encrypted JPEG images. EURASIP Journal on Information Security, 2016, 2016(1): 1–9

[33] Yan H, Li X, Wang Y, Jia C. Centralized duplicate removal video storage system with privacy preservation in IoT. Sensors, 2018, 18(6): 1814

[34] Li Y, Wang G, Nie L, Wang Q, Tan W. Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recognition, 2018, 75: 51–62

[35] Ferreira B, Rodrigues J, Leitao J, Domingos H. Practical privacypreserving content-based retrieval in cloud image repositories. IEEE Transactions on Cloud Computing, 2017, 99: 1

[36] Zhang L, Jung T, Liu C, Ding X, Li X, Liu Y. EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. In: Proceedings of the 35th International Conference on Distributed Computing Systems. 2015, 308–317

[37] Hsu C Y, Lu C S, Pei S C. Image feature extraction in encrypted domain with privacy-preserving SIFT. IEEE Transactions on Image Processing, 2012, 21(11): 4593–4607

[38] Hu S, Wang Q, Wang J, Qin Z, Ren K. Securing SIFT: privacypreserving outsourcing computation of feature extractions over encrypted image data. IEEE Transactions on Image Processing, 2016, 25(7): 3411–3425

[39] Li T, Chen W, Tang Y, Yan H. A homomorphic network coding signature scheme for multiple sources and its application in IoT. Security and Communication Networks, 2018

[40] Juan Ram T P, Fernando P G. Secure signal processing in the cloud: enabling technologies for privacy-preserving multimedia cloud processing. IEEE Signal Processing Magazine, 2013, 30(2): 29–41

[41] Lin Q, Yan H, Huang Z, Chen W, Shen J, Tang Y. An ID-based linearly homomorphic signature scheme and its application in blockchain. IEEE Access, 2018, 6(1): 20632–20640

[42] Fridrich J. Symmetric ciphers based on two-dimensional chaotic maps. International Journal of Bifurcation and Chaos, 1998, 8(6): 1259–1284

[43] Belazi A, El-Latif A A A, Belghith S. A novel image encryption scheme based on substitution-permutation network and chaos. Signal Processing, 2016, 128: 155–170

[44] Ping P, Xu F, Mao Y, Wang Z. Designing permutation-substitution image encryption networks with Henon map. Neurocomputing, 2018, 283(29): 53–63

[45] Li X, Zhang G, Zhang X. Image encryption algorithm with compound chaotic maps. Journal of Ambient Intelligence and Humanized Computing, 2015, 6(5): 563–570

[46] Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques. 1999, 223–238

[47] Lu R, Liang X, Li X, Lin X, Shen X. EPPA: an efficient and privacypreserving aggregation scheme for secure smart grid communications. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(9): 1621–1631

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature