FHH匿名多接收者加密评论:安全性的再评估

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本文主要关注于"关于FHH匿名多接收器加密的评论"这一主题,发表在《国际网络安全》杂志2014年7月第16卷第4期,页码285-288。FHH匿名多接收者加密方案是由Fan等人提出的一种新型加密技术,其宣称能够同时提供保密性和匿名性。然而,作者Zhenhua Chen、Shundong Li、Chunzhi Wang 和 Mingwu Zhang 在这封信件中提出了对FHH方案的批评,指出该方案并不满足预设的安全特性。 首先,他们指出了FHH方案在实现匿名性方面存在缺陷。按照定义,一个理想的匿名多接收者加密系统应该确保即使攻击者无法确定消息发送者的真实身份,也能对消息进行解密。但作者论证了FHH设计并未达到这个标准,即它实际上只实现了较弱的保密性(IND-CPA),这意味着即使在选择性明文攻击(Chosen Plaintext Attack, CPA)的情景下,攻击者仍有可能推断出部分信息,破坏了完全的匿名性。 其次,他们提及了FHH方案依赖于拉格朗日插值等技术,这些技术在多接收者加密中扮演关键角色。然而,他们分析了这些技术在FHH方案中的具体应用,发现可能存在漏洞或不足,从而影响了系统的安全性。 此外,文章还提到了匿名多接收者身份基加密(Anonymous Multireceiver Identity-Based Encryption, AMIBE)的应用领域,如付费电视和安全电子邮件等,强调了这种技术在实际场景中的重要性。但是,对于FHH方案的安全质疑,意味着在采用此类加密系统时,必须对其安全性有深入理解和谨慎评估。 这篇评论揭示了FHH匿名多接收器加密方案在设计上的局限,并提出了改进的方向,这对于研究人员和潜在用户来说都是有价值的信息。对于那些依赖这类技术的系统开发者和安全专业人士来说,了解并解决这些问题至关重要,以确保信息安全的完整性。

GOAL Perform a Poisson regression to predict the number of people in a househouse based on the age of the head of the household. DATA The Philippine Statistics Authority (PSA) spearheads the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three years, is aimed at providing data on family income and expenditure, including levels of consumption by item of expenditure. The data, from the 2015 FIES, is a subset of 1500 of the 40,000 observations (Philippine Statistics Authority 2015). The data set focuses on five regions: Central Luzon, Metro Manila, Ilocos, Davao, and Visayas. The data is in the file fHH1.csv. Each row is a household, and the follow variables are recorded: • location: where the house is located (Central Luzon, Davao Region, Ilocos Region, Metro Manila, or Visayas) • age: the age of the head of household • total: the number of people in the household other than the head • numLT5: the number in the household under 5 years of age • roof: the type of roof in the household (either Predominantly Light/Salvaged Material, or Predominantly Strong Material. STEPS 1. Read in the dataset. 2. Produce a bar-chart of total 3. Produce a scatter-plot of total against age - add a smoothing line. 4. Fit the Poisson regression total ∼ age 5. Interpret the coefficient of age. 6. Obtain the Pearson residuals. Plot these against age. Is the model adequate? 7. Fit the Poisson regression total ∼ age + age2 8. Repeat the residual plots for the new model. 9. Compare the models using a likelihood ratio test, and AIC. 10. Calculate the predicted values for model M2. What is the age of the head of the household associated with the largest fitted value 使用R语言

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