Cloud storage provides highly available and low cost resources to users. However, as massive amounts of outsourced data grow rapidly, an effective data deduplication scheme is necessary. This is a hot and challenging ...
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Cloud storage provides highly available and low cost resources to users. However, as massive amounts of outsourced data grow rapidly, an effective data deduplication scheme is necessary. This is a hot and challenging field, in which there are quite a few researches. However, most of previous works require dual-server fashion to be against brute-force attacks and do not support batch checking. It is not practicable for the massive data stored in the cloud. In this paper, we present a secure batch deduplication scheme for backup system. Besides, our scheme resists the brute-force attacks without the aid of other servers. The core idea of the batch deduplication is to separate users into different groups by using short hashes. Within each group, we leverage group key agreement and symmetric encryption to achieve secure batch checking and semantically secure storage. We also extensively evaluate its performance and overhead based on different datasets. We show that our scheme saves the data storage by up to 89.84%. These results show that our scheme is efficient and scalable for cloud backup system and can also ensure data confidentiality. IEEE
In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption m...
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In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure *** of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB *** this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)*** proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three *** proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment *** achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 *** findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.
As technology has evolved, digital images have become increasingly popular. However, these images often exhibit a high degree of correlation and redundancy among pixels, which creates specific requirements for applica...
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Recently there has been a large amount of research designing mechanisms for auction scenarios where the bidders are connected in a social network. Different from the existing studies in this field that focus on specif...
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In the rapidly evolving field of healthcare, accurate clinical predictions are paramount for effective disease management and treatment planning. This paper introduces a novel ensemble machine learning model that util...
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With the rapid evolution of technology, the demand for mobile devices capable of executing computationally intensive and time-sensitive applications has surged. However, these devices are often constrained by limited ...
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Row-diagonal parity (RDP) code is a classical (k + 2, k) systematic maximum distance separable (MDS) array code with k ≤ L - 1 under sub-packetization level l = L - 1, where L is a prime integer. When k = L-1, its en...
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Federated learning (FL) collaboratively trains global models while preserving private data locally, making it an ideal privacy-preserving learning technique. However, recent studies have shown that FL poses risks of s...
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This report mainly concentrates on cancer prediction, particularly focusing on breast cancer prediction, which remains a significant challenge in healthcare. Improving patient outcomes requires early identification an...
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Mehta and Panigrahi (FOCS 2012, IEEE, Piscataway, NJ, 2012, pp. 728-737) introduce the problem of online matching with stochastic rewards, where edges are associated with success probabilities and a match succeeds wit...
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