An extended framework of privacy-preserving computation with flexible access control

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Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?

Collaborative inference has recently emerged as an attractive framework for applying deep learning to Internet of Things (IoT) applications by splitting a DNN model into several subpart models among resource-constrained IoT devices and the cloud. However, the reconstruction attack was proposed recently to recover the original input image from intermediate outputs that can be collected from local models in collaborative inference. For addressing such privacy issues, a promising technique is to adopt differential privacy so that the intermediate outputs are protected with a small accuracy loss. In this paper, we provide the first systematic study to reveal insights regarding the effectiveness of differential privacy for collaborative inference against the reconstruction attack. We specifically explore the privacy-accuracy trade-offs for three collaborative inference models with four datasets (SVHN, GTSRB, STL-10, and CIFAR-10). Our experimental analysis demonstrates that differential privacy can practically be applied to collaborative inference when a dataset has small intra-class variations in appearance. With the (empirically) optimized privacy budget parameter in our study, the differential privacy technique incurs accuracy loss of 0.476%, 2.066%, 5.021%, and 12.454% on SVHN, GTSRB, STL-10, and CIFAR-10 datasets, respectively, while thwarting the reconstruction attack.Comment: Accepted in Wireless Network

A Secure Storage Management & Auditing Scheme for Cloud Storage

Cloud computing is an evolving domain that provides many on-demand services that are used by many businesses on daily basis. Massive growth in cloud storage results in new data centers which are hosted by a large number of servers. As number of data centers increases enormous amount of energy consumption also increases. Now cloud service providers are looking for environmental friendly alternatives to reduce energy consumption. Data storage requires huge amount of resources and management. Due to increasing amount of demand for data storage new frameworks needed to store and manage data at a low cost. Also to prevent data from unauthorized access cloud service provider must provide data access control. Data access control is an effective way to ensure data storage security within cloud. For data storage cost minimization we are using DCT compression technique to ensure data compression without compromising the quality of the data. For data access control and security asymmetric cryptographic algorithm RSA is used. For data auditing we have used MD5 with RSA to generate digital signatures, In proposed work we tried to cover all attributes in terms of efficiency, performance and security in cloud computing

Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality

The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs