Abstract
In cloud storage, existing image fuzzy deduplication technology often adopts a cloud-based deduplication method, which, although it improves the image’s deduplication efficiency, ignores the client’s communication overhead. Therefore, to further reduce the bandwidth consumption caused by redundant image uploading, researchers use similar images to extract the same features and employ image features as encryption keys to achieve cross-user deduplication. Although this approach reduces the communication overhead, it increases the risk of side-channel attacks and threatens the image’s privacy. Thus, this paper proposes a cross-user deduplication scheme based on image content decomposition to solve the privacy concern. Specifically, by acquiring the image’s frequency characteristics, the base data representing the image’s main contents and the deviation data representing the image’s details are decomposed from the image. Then, we use the cross-user deduplication method for the base data deduplication and the cloud side deduplication method for the deviation data deduplication. The implementation demonstrates that the developed scheme improves the deduplication efficiency under the premise of effectively resisting side-channel attacks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shin, Y., Koo, D., Hur, J.: A survey of secure data deduplication schemes for cloud storage systems. ACM Comput. Surv. 49(4), 1–38 (2017). https://doi.org/10.1145/3017428
Zhang, J., Bhuiyan, M., Yang, X., et al.: Trustworthy target tracking with collaborative deep reinforcement learning in EdgeAI-aided IoT. IEEE Trans. Industr. Inf. 18(2), 1301–1309 (2022)
JiweiZhang, M., et al.: AntiConcealer: reliable detection of adversary concealed behaviors in EdgeAI-assisted IoT. IEEE Internet Things J. 9(22), 22184–22193 (2022). https://doi.org/10.1109/JIOT.2021.3103138
Bellare, M., Keelveedhi, S., Ristenpart, T.: Message-locked encryption and secure deduplication. In: Johansson, T., Nguyen, P.Q. (eds.) Advances in Cryptology – EUROCRYPT 2013. LNCS, vol. 7881, pp. 296–312. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38348-9_18
Gang, H., Yan, H., Xu, L.: Secure image deduplication in cloud storage. In: Khalil, I., Neuhold, E., Tjoa, A.M., DaXu, L., You, I. (eds.) Information and Communication Technology. LNCS, vol. 9357, pp. 243–251. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24315-3_25
Douceur, J., Adya, A., Bolosky, B., et al.: Reclaiming space from duplicate files in a server-less distributed file system. In: 22nd international conference on distributed computing systems (DCS), pp. 617–624, Vienna, Austria (2002)
Zheng, Y., Pan, J.: A duplicate data detection algorithm based on sliding blocking. Comput. Eng. 42(2), 38–44 (2016)
Agarwala, A., Singh, P., Atrey, P.: Client side secure image deduplication using DICE protocol. In: IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 412–417, Miami, USA (2018)
Ramaiah, P., Mohan, K.: De-duplication of photograph images using histogram refinement. In: IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 391–395, Trivandrum, Kerala (2011)
Sun, Z., Lai, J., Chen, X., Tan, T. (eds.): Biometric Recognition. LNCS, vol. 7098. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25449-9
Chen, M., Wang, Y., Zou, X., et al.: A duplicate image deduplication approach via Haar wavelet technology. In: IEEE 2nd International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 624–628, Hangzhou, China (2012)
Li, J., Chen, X., Li, M., et al.: Secure deduplication with efficient and reliable convergent key management. IEEE Trans. Parallel Distrib. Syst. 25(6), 1615–1625 (2014)
Li, D., Yang, C., Jiang, Q., et al.: A client-based image fuzzy deduplication method supporting proof of ownership. Chinese J. Comput. 41(6), 1267–1283 (2018)
Takeshita, J., Karl, R., Jung, T.: Secure single-server nearly-identical image deduplication. In: International Conference on Computer Communications and Networks (ICCCN), pp. 1–6, Honolulu, USA (2020)
Li, X., Li, J., Huang, F.: A secure cloud storage system supporting privacy-preserving fuzzy deduplication. Soft. Comput. 20(4), 1437–1448 (2015). https://doi.org/10.1007/s00500-015-1596-6
Jiang, T., Yuan, X., Chen, Y., et al.: FuzzyDedup: secure fuzzy deduplication for cloud storage. IEEE Trans. Depend. Secur. Comput. 1–18 (2022)
Zuo, P., Hua, Y., Wang, C., et al.: Mitigating traffic-based side channel attacks in bandwidth-efficient cloud storage. In: IEEE International Parallel and Distributed Processing Symposium (IPDPS 2018) (2018)
Tang, X., Zhang, Y., Zhou, L., et al.: Request merging based cross- user deduplication for cloud storage with resistance against appending chunks attack. Chin. J. Electron. 30(2), 199–209 (2021)
Tang, X., Chen, X., Zhou, R., et al.: Marking based obfuscation strategy to resist side channel attack in cross-user deduplication for cloud storage. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom), Wuhan, China (2022)
Tang, X., Liu, Z., Shao, Y., et al.: Side channel attack resistant cross-user generalized deduplication for cloud storage. In: IEEE International Conference on Communications (ICC), pp. 998–1003, Seoul, South Korea (2022)
Acknowledgment
This work was specially supported by National Natural Science Foundation of China (62102113) and parted supported by Construction of advanced disciplines for University of International Relations (2021GA08), Fundamental Research Funds for the Central Universities, University of International Relations (3262022T20) and Student Academic Research Training Project of University of International Relations (3262022SWA01).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, X., Tang, X., Jin, L., Chen, X., Zhou, Z., Zhang, S. (2022). Secure Cross-User Fuzzy Deduplication for Images in Cloud Storage. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_20
Download citation
DOI: https://doi.org/10.1007/978-981-19-8991-9_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8990-2
Online ISBN: 978-981-19-8991-9
eBook Packages: Computer ScienceComputer Science (R0)