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A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.
相似文献The proposed system portrays the application space examination of a diverse cryptosystem processor with dynamic reconfiguration abilities. It is appropriate to a variety of signal processing application domains namely telecommunications, image processing, video coding and cryptographic processing. To differentiate between application spaces of the processor, the performance is correlated with cutting edge devices, taking ability to program, energy efficiency and computational potential as the important factors. In general the conventional method of computation is processed by means of Virtual Secure Circuit (VSC) on Advanced Encryption Standard (AES) and performance of the device Field Programmable Gate Array (FPGA) after implementation is analyzed in terms of delay and throughput. In the conventional method area overhead and power consumption are less where as the architecture lags in performance and throughput. It has been overcome through the fully parallel pipelined Architecture of the VSC on AES which outperforms the existing method in terms of performance and throughput. The energy efficiency and performance are considerably more important than processor that are used for general purpose, while still preserving a Convenient approach of programming that mainly bank on software oriented languages. The exploit of VSC based AES is to formulate the cryptographic processor held against Side Channel Attacks like attacks based on power supply and electromagnetic signals. Then the experimental result shows the promising outcomes when compared to previous methods.
相似文献A fuzzy set based cost model for additive manufacturing is proposed. The model is illustrated through an example of selective laser sintering (SLS), a commonly used additive manufacturing (AM) process. Initially, a deterministic cost model is developed. It is then converted to a fuzzy set based model by considering uncertainty in build time estimation and cost components. For the specific case of SLS, a build time estimation procedure is developed as a part of the cost model. A method to evaluate the reliability of the cost estimate in AM and its significance is also introduced. The cost model is illustrated through two examples of products manufactured by SLS.
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