Ni, ChunchunCang, Li ShanGope, ProsantaMin, Geyong...
12页
查看更多>>摘要：The growth of big data can increase risks of re-identification in complex IoT environment. Data anonymization is widely used to prevent shared data from being re-identified private or sensitive information from anonymized data with other available data. It is very important to understand the risks of re-identification of results and process of commonly used anonymization techniques before properly mitigating. This work proposes a data anonymisation evaluation framework that can evaluate commonly used data anonymization algorithms in terms of privacy preserving level, data utility, and performance. Specifically, data utility, privacy metrics, and information loss were used to evaluate data anonymization schemes that can further optimise the data anonymization process to keep risks of re identification low. Experiment results demonstrate that the proposed solution can effectively evaluate the performance and de-identification features that can help to prevent inappropriate usage of anonymized data. CO 2022 Published by Elsevier Inc.
查看更多>>摘要：A well exploitation of computational resource is essential when solving high-dimensional multiobjective problems (MOPs). Since many recent strategies presented for decision space and objective space are in consideration of searching efficiency, the collaboration between the two spaces is a promising approach for high-dimensional optimization. In this article, resource allocation strategies for both decision and objective spaces are readjusted and cooperated for complex MOPs. A metric-based variable partition strategy is introduced and a simple reference adaptation strategy is adopted to specify the searching orientations in the decision space and the objective space respectively. Subsequently, based on the above-mentioned basic techniques, three different evolutionary strategies are further designed to strengthen the directional convergence and preserve the diversity of target regions in a collaborative fashion. Several benchmark instances and a practical optimization problem are adopted in the experimental study. The effectiveness and rationality of the proposed resource allocation approach have been demonstrated by the experimental results.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：As the significant expansion of classical rough sets, local rough sets(LRS) is an effective model for processing large-scale datasets with finite labels. However, the process of establishing a category of monotonic uncertainty measure with strong distinguishing ability for LRS remains ambiguous. To construct this model, both the monotonicity of local lower approximation set and the local structure of granularity should be considered. First, the monotonicity of local rough approximation measure(LRAM) is established by the local lower and upper approximation sets. Subsequently, the local knowledge distance(LKD) is proposed to measure the uncertainty derived from the disparities between local upper and lower approximation sets. The more rational uncertainty measure associated LRAM with LKD is designed as a feature selection algorithm. Eventually, the experiments reflect the feasibility of the developed uncertainty measure.(c) 2022 Elsevier Inc. All rights reserved.
NSTL
Elsevier