SecDedup: Secure Encrypted Data Deduplication With Dynamic Ownership Updating

Zhang, Shuguang; Xian, Hequn; Wang, Liming

doi:10.1109/access.2020.3023387

Cited by 9 publications

(11 citation statements)

References 34 publications

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“…Along with the growth of Internet users, new security issues arise while traditional security issues become more severe [5]. On one hand, the new paradigms could bring facilities and impressive experiences, such as data forecasting [6], content sharing [7] [8] and computation offloading [9]. On the other hand, the challenge of defense strategy is elevated, and sophisticated protection mechanism should be involved in every aspect from end devices to infrastructures, e.g., mobile devices [10], edge server [11], cloud data center [12] [13], and blockchain [14].…”

Section: Related Work a Privacy Leakage And Protectionmentioning

confidence: 99%

Encrypted Live Streaming Channel Identification With Time-Sync Comments

et al. 2022

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The time-sync comments have been prevalent in modern live streaming systems to provide a real-time interaction experience for viewers. Whereas, the time-sync comments traffic can also act as a delicate fingerprint of encrypted live channels, leading to potential risks of privacy leakage. Most of previous video channel identification strategies with video bitrate-based fingerprint presume strict requirements on the implementation environments, which often assume that there is no interference from irrelevant traffic flows or network conditions. However, the time-sync comments sessions are distinct from other irrelevant traffic flows, and the traffic pattern is resilient to various network conditions, e.g., bandwidth limitation and transmission delay. In this paper, we design a system for encrypted live channel identification with timesync comments traffic analysis. Specifically, both the inter-application and inner-application traffic filters are proposed to eliminate the irrelevant traffic flows, respectively. Further, a comment rate estimation method is developed through investigation of relationship between comment number, comment length and packet length. Finally, the dynamic time warping algorithm is improved for similarity matching in delay tolerant environment. In order to evaluate the system performance, we setup the prototype system with AWS EC2 server and utilize the real world trace data from Youtube and BiliBili. The experimental results show that the accuracy of the filter can reach 93.2%, and the accuracy of the comment rate estimation method can reach up to 91%. The match accuracy between fingerprint and comment rate can reach 92.1% within 200 seconds eavesdropping, which is 2% higher than using bitrate fingerprint and traffic pattern in the latest research, and can be increased to 98.2% when the eavesdropping time extends to 500 seconds.

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Section: Related Work a Privacy Leakage And Protectionmentioning

confidence: 99%

Encrypted Live Streaming Channel Identification With Time-Sync Comments

et al. 2022

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“…1. Total compression rates for different chunk sizes using Bonsai (this work), Yggdrasil [10], SecDedup [15], Brotli [3], and combination of these techniques (lower is better).…”

Section: Compression Ratementioning

confidence: 99%

“…Finally, to showcase the performance impact of Bonsai on a cloud storage system, we used a DVI dataset of 10 GB and collected simulations for compression ratios achieved by Bonsai, Yggrdasil [10], data deduplication (DD) [15], and Brotli [3]. The comparison is illustrated in Fig.…”

Section: Compression Ratementioning

confidence: 99%

“…• Increasing performance and throughput: This line of research includes, but is not limited to optimization of deduplication [15], acceleration of computational tasks [24], reduction of the access time to metadata [25], and improvements of the chunking procedure based on the content [26], [27]. • Reducing the storage requirement: These solutions focus on reducing the fingerprint of the data.…”

Section: Related Workmentioning

confidence: 99%

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Bonsai: A Generalized Look at Dual Deduplication

Sehat¹,

Lindskov²,

Mørup³

et al. 2022

Preprint

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Cloud Service Providers (CSPs) offer a vast amount of storage space at competitive prices to cope with the growing demand for digital data storage. Dual deduplication is a recent framework designed to improve data compression on the CSP while keeping clients' data private from the CSP. To achieve this, clients perform lightweight information-theoretic transformations to their data prior to upload. We investigate the effectiveness of dual deduplication, and propose an improvement for the existing state-of-the-art method. We name our proposal Bonsai as it aims at reducing storage fingerprint and improving scalability. In detail, Bonsai achieves (1) significant reduction in client storage, (2) reduction in total required storage (client + CSP), and (3) reducing the deduplication time on the CSP. Our experiments show that Bonsai achieves compression rates of 68% on the cloud and 5% on the client, while allowing the cloud to identify deduplications in a time-efficient manner. We also show that combining our method with universal compressors in the cloud, e.g., Brotli, can yield better overall compression on the data compared to only applying the universal compressor or plain Bonsai. Finally, we show that Bonsai and its variants provide sufficient privacy against an honest-but-curious CPS that knows the distribution of the Clients' original data.

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“…Second, to address dynamic ownership management, many schemes [4], [15]- [19] were proposed by using either trusted third party or semi-trusted third party to do proxy reencryption work, such as Authority Party (AP ) or The Public Cloud Provider (P ub − CSP ). On the one hand, it could be difficult to implement a trusted third party in practical applications [20]. On the other hand, some schemes can not resist collusion attacks when the third party colludes with unauthorized users.…”

Section: Introductionmentioning

confidence: 99%

A Secure and Efficient Data Deduplication Scheme with Dynamic Ownership Management in Cloud Computing

Yang

Zhu

et al. 2022

2022 IEEE International Performance, Computing, and Communications Conference (IPCCC)

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Encrypted data deduplication is an important technique for saving storage space and network bandwidth, which has been widely used in cloud storage. Recently, a number of schemes that solve the problem of data deduplication with dynamic ownership management have been proposed. However, these schemes suffer from low efficiency when the dynamic ownership changes a lot. To this end, in this paper, we propose a novel server-side deduplication scheme for encrypted data in a hybrid cloud architecture, where a public cloud (Pub-CSP) manages the storage and a private cloud (Pri-CSP) plays a role as the data owner to perform deduplication and dynamic ownership management. Further, to reduce the communication overhead we use an initial uploader check mechanism to ensure only the first uploader needs to perform encryption, and adopt an access control technique that verifies the validity of the data users before they download data. Our security analysis and performance evaluation demonstrate that our proposed serverside deduplication scheme has better performance in terms of security, effectiveness, and practicability compared with previous schemes. Meanwhile, our method can efficiently resist collusion attacks and duplicate faking attacks.

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SecDedup: Secure Encrypted Data Deduplication With Dynamic Ownership Updating

Cited by 9 publications

References 34 publications

Encrypted Live Streaming Channel Identification With Time-Sync Comments

Encrypted Live Streaming Channel Identification With Time-Sync Comments

Bonsai: A Generalized Look at Dual Deduplication

A Secure and Efficient Data Deduplication Scheme with Dynamic Ownership Management in Cloud Computing

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