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Plant studies in Himachal Pradesh,Western Himalaya: a systematic review

Journal of Mountain Science 2021年第7期18卷 1856-1873页

作者： Himanshu BARGALI Manisha MATHELA rupali sharma Monika sharma Dani YAMING Amit KUMAR Contributed equally to this work Wildlife Institute of India Post box#18Dehradun 248002UttarakhandIndia

Owing to a predicted rise in temperature,increase in precipitation and the escalating anthropogenic stresses,the Himalayan ecosystems are the most threatened non-polar regions of the *** the past,there have been racing extinctions and range shifts of several life forms in this region,particularly of the plant ***,consistent qualitative and quantitative records on botanical baseline data on a regular basis are *** current communication attempts to provide a detailed account on the current state of knowledge and research gaps in floristic studies of Himachal Pradesh,Western *** on an extensive review of more than 140 studies in the form of scientific peer reviewed literature on floristics,medicinal and aromatic plants(MAPs),rare,endangered,threatened and endemic species and ethno-botany,it is revealed that the number of floristic studies in Himachal Pradesh have surged in the past years but studies pertaining to the lower plants remain ***,the remote valleys including micro-watersheds lying in the coldarid regions are relatively less studied in terms of available growing stock and population status of *** traditional ethno-botanical knowledge has been the major concern and,many threatened MAPs need to be accessed globally following IUCN ***,documentation of the conservation status of key MAPs,setting aside conservation areas around viable populations of threatened species,developing germplasm banks of the priority MAPs and monitoring the impacts of climate change has been suggested.

关键词： Biodiversity Ethno-botany Flora Medicinal plants Threatened plants Western Himalaya

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Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

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Computers, Materials & Continua 2023年第4期75卷 2171-2190页

作者： rupali Gangarde Amit sharma Ambika Pawar Department of CSE Lovely Professional UniversityPhagwara144411India Department of CSE Symbiosis Institute of Technology(SIT)Affiliated to Symbiosis International(Deemed University)Pune412115India School of Computer Applications Lovely Professional UniversityPhagwara144411India Learning&Development Persistent UniversityPersistent SystemsPune411057India

Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy *** enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold *** threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

关键词： Enhanced clustering online social network k-anonymity t-closeness l-diversity privacy preservation

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