K-nearest neighbors (KNN) classification plays a significant role in various applications due to its interpretability. The accuracy of KNN classification relies heavily on large amounts of high-quality data, which are often distributed among different parties and contain sensitive information. Dozens of privacy-preserving frameworks have been proposed for performing KNN classification with data from different parties while preserving data privacy. However, existing privacy-preserving frameworks for KNN classification demonstrate communication inefficiency in the online phase due to two main issues: (1) They suffer from huge communication size for secure Euclidean square distance computations. (2) They require numerous communication rounds to select the k nearest neighbors. In this paper, we present Kona, an efficient privacy-preserving framework for KNN classification. We resolve the above communication issues by (1) designing novel Euclidean triples, which eliminate the online communication for secure Euclidean square distance computations, (2) proposing a divide-and-conquer bubble protocol, which significantly reduces communication rounds for selecting the k nearest neighbors. Experimental results on eight real-world datasets demonstrate that Kona significantly outperforms the state-of-the-art framework by 1.1x similar to 3121.2x in communication size, 16.7x similar to 5783.2x in communication rounds, and 1.1x similar to 232.6x in runtime.

Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with privacy preservation. In recent years, in order to apply MPL in more practical scenarios, various MPC-friendly models have been proposedto reduce the extraordinary communication overhead of MPL. Within the optimization of MPC-friendly models, a critical element to tackle the challenge is profiling the communication cost of models. However, the current solutions mainly depend on manually establishing the profiles to identify communication bottlenecks of models, often involving burdensome human efforts in a monotonous procedure. In this paper, we propose HawkEye, a static model communication cost profiling framework, which enables model designers to get the accurate communication cost of models in MPL frameworks without dynamically running the secure model training or inference processes on a specific MPL framework. Firstly, to profile the communication cost of models with complex structures, we propose a static communication cost profiling method based on a prefix structure that records the function calling chain during the static analysis. Secondly, HawkEye employs an automatic differentiation library to assist model designers in profiling the communication cost of models in PyTorch. Finally, we compare the static profiling results of HawkEye against the profiling results obtained through dynamically running secure model training and inference processes on five popular MPL frameworks, CryptFlow2, CrypTen, Delphi, Cheetah, and SecretFlow-SEMI2K. The experimental results show that HawkEye can accurately profile the model communication cost without dynamic profiling.

Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with privacy preservation. The training process essentially involves frequent dataset splitting according to the splitting criterion (e.g. Gini impurity). However, existing multi-party training frameworks for decision trees demonstrate communication inefficiency due to the following issues: (1) They suffer from huge communication overhead in securely splitting a dataset with continuous attributes. (2) They suffer from huge communication overhead due to performing almost all the computations on a large ring to accommodate the secure computations for the splitting criterion. In this paper, we are motivated to present an efficient three-party training framework, namely Ents, for decision trees by communication optimization. For the first issue, we present a series of training protocols based on the secure radix sort protocols [17] to efficiently and securely split a dataset with continuous attributes. For the second issue, we propose an efficient share conversion protocol to convert shares between a small ring and a large ring to reduce the communication overhead incurred by performing almost all the computations on a large ring. Experimental results from eight widely used datasets show that Ents outperforms state-of-the-art frameworks by 5.5x similar to 9.3x in communication sizes and 3.9x similar to 5.3x in communication rounds. In terms of training time, Ents yields an improvement of 3.5x similar to 6.7x. To demonstrate its practicality, Ents requires less than three hours to securely train a decision tree on a widely used real-world dataset (Skin Segmentation) with more than 245,000 samples in the WAN setting.

针对2006-2010年广西电网超高压输电公司统计的线路遭遇山火状况,研究山火引起线路跳闸的环境特征,包括地形因素、可燃物状况以及山火行为特征,提出应对的控制和防治措施.并以广西地区为研究对象,获取研究区内气象站地面气象资料月值数据,包括平均气温、降水量、最小相对湿度以及1999-2004年的森林火灾数据,分析气候因素对山火发生的影响.结果表明:广西森林火灾主要发生在冬季和春季,分别占总次数的37.4％和37.0％,夏季很少发生火灾;冬季发生火灾的过火面积所占比例最高,占46.8％,其次为春季,占33.7％;其中,百色市、柳州市、梧州市发生火灾较多,山火造成线路跳闸率分别为9.2％、125.0％、4.7％.山火引发跳闸地点可燃物类型主要为杂树、杂草、松树和杉树,树木树龄多为3-21年(3年以上居多),胸径2～45 cm(10 cm以上居多),树高2.0～23.0 m(5.0～10.0 m居多),跳闸地点均出现在阳坡,坡度为16°～80°,其中<30°的缓坡和30°～60°的斜坡出现次数最多,分别占56％和36％;引起高压线路跳闸最主要的火灾类型是快速、中度地表火以及稳进树冠火,蔓延速度在2.1 m/min以上,火焰高度在1.5 m以上,火强度在750 W/m2以上,建议根据山火发生的特殊性,加强山火高发地区的早期预警、火灾监测和可燃物管理等防控措施,减少山火引起输电线路跳闸,保障居民正常生活和社会稳定.

在分析系统设计过程、典型程序及相应的计算机环境的基础上,构建满足森林防火指挥部门工作需求的地理信息辅助决策支持系统,实现森林防火信息可视化管理.将国家卫星监测中心监测并传递到各个林业基层部门的热点信息(经纬度坐标和影像信息),以林火图标的方式快速叠加到1∶5万数字地形图或林火专题图上,或读取热点文件中的热点信息,以林火图标方式定位于数字地形图或林火专题图上,之后查询热点周边的详细信息.热点半径可由人工手动拖拉鼠标确定生成,也可由录入具体半径生成(这些点和圆是一种几何图形),再利用空间过滤技术,对地图窗口中可选择的图层过滤,检索出落在这个圆中的地理信息.

森林火灾具有突发性、破坏性,而且对森林资源和国民经济造成巨大损失.利用主成分分析法获取全国范围内森林火灾危害程度评价综合指标,对各省份进行聚类分析.结果表明:受林火危害影响程度较大的是云南省,其次是福建、广西两省.加大森林高火险地区的森林消防力量,合理配置人力、物力资源,并强化居民安全用火意识,可有效减少森林火灾的危害.

生态旅游逐渐被广大民众所接受并得到了快速发展.在全球气候日益变暖、森林防火形势极为严峻的背景下,森林防火的宣传教育与生态旅游结合已成为必然.对我国生态旅游过程中森林防火的宣传教育进行了探讨.分析表明,寓教于游可以使游客与旅游景区产生良性互动,还可以从深层次上加深人们的环境道德观念,为生态旅游发展提供强大的智力支持.

介绍了我国航空护林管理体制的现状,分析了当前我国航空护林管理体制存在的问题以及我国航空护林面临的形势,提出了改革我国航空护林管理体制的指导思想和建议.

本文介绍了加拿大SEI工业股份公司生产的几种水泵吊桶,对水泵吊桶的分类及特点、水源要求、吸水速度等进行了叙述,同时对我国森林航空消防引进水泵吊桶的作用和意义进行简单阐述,认为水泵吊桶的引进可以提高我国森林航空消防吊桶灭火的工作效率和经济效益,为实现森林防火“打早、打小、打了”的目标有着重要的作用和意义.

介绍了美国森林航空消防局的职能、森林航空消防发展历史、开展的森林航空消防业务项目以及各类别森林航空消防基地的数量及分布位置,同时还介绍了美国林务局近年来森林航空消防的飞行时间及飞机数量、森林航空消防飞行安全情况及安全管理体系。