New LabPlot tutorial:
In this short video you'll learn how to fit a distribution to data.
#DistributionFitting #GaussianDistribution, #Log-normalDistribution #ProbabilityDistribution #PoissonDistribution #Binomial #Distribution #ExponentialDistribution #MaximumLikelihood
#distributionfitting #gaussiandistribution #log #poissondistribution #binomial #distribution #exponentialdistribution #maximumlikelihood #probabilitydistribution
New LabPlot tutorial
https://tube.kockatoo.org/w/9Kmqeefqw35EpEZj914N5p
In this short video you'll learn how to how to fit a distribution to data.
#DistributionFitting #GaussianDistribution, #Log-normalDistribution #Probability Distribution #PoissonDistribution #Binomial #Distribution #ExponentialDistribution #MaximumLikelihood
#distributionfitting #gaussiandistribution #log #probability #poissondistribution #binomial #distribution #exponentialdistribution #maximumlikelihood
Do you want to know how to fit a probability distribution to data? Watch the latest LabPlot's video tutorial.
https://www.youtube.com/watch?v=g0OQrAVwsTw
#DistributionFitting #ProbabilityDistribution #Distribution #StatisticalDistribution #Gaussian #Log-normal #Probability #Poisson #Binomial #Exponential #MaximumLikelihood #LabPlot
#labplot #maximumlikelihood #exponential #binomial #poisson #Probability #log #gaussian #statisticaldistribution #distribution #probabilitydistribution #distributionfitting
Now (7pm ET Wed) watch https://youtu.be/QS0VmCD9YLU(FEEL FREE TO SUBSCRIBE TO YOUTUBE
@hajiaghayi
FOR FUTURE LESSONS) Lesson 14: Introduction to Algorithms by Mohammad Hajiaghayi: We talk about #Probability (Part 2) useful for designing #randomized #algorithms
#algorithms, #design, #induction, #recursive, #randomizedalgorithms, #probability, #randominput ,
#probabilitytheory, #randomvariables, #expectations, #variance, #Bernoulli, #Binomial, #Poisson, #Normaldistribution, #Gaussian, #Python, #numpy.random, #scipy.stats, #correlation, #Pearson, #spearman, #geeksforgeeks , #hackerrank, #leetcode, #cs, #computerscience
#computerscience #cs #leetcode #hackerrank #geeksforgeeks #spearman #pearson #correlation #scipy #numpy #python #gaussian #normaldistribution #poisson #binomial #Bernoulli #variance #expectations #randomvariables #probabilitytheory #randominput #randomizedalgorithms #recursive #induction #design #algorithms #randomized #probability
I recently read a very interesting paper on Leakage-Abuse Attacks against Order-Preserving Encryption (OPE) schemes and Order-Revealing Encryption (ORE) Schemes.
In this paper, the researchers show how the widely used encryption schemes are inadequate. Here are some snippets from the paper.
Order-preserving encryption (#OPE) - ensures that Ek(m1)<Ek(m2) for m1<m2 and Ek the encryption algorithm. Most widely used scheme is #BCLO.
Order-revealing encryption (#ORE) - reveals ordering relations by way of a public comparison function that operates on pairs of plaintexts. Most widely used scheme is #CLWW.
Popular belief is that OPE and ORE schemes remain secure in practice for plaintext data drawn from larger domains, and practitioners could simply avoid using OPE for small-domain data.
The researchers used a non-crossing attack (min-weight non-crossing #bipartite matching) which runs in only a few hours, even for the largest target dataset, against real-world datasets using the BCLO scheme to encrypt a set of first names.
Using this attack they were able to recover almost half the data set. The leakage was even worse for last names, with almost 97% of last names trivially recoverable.
#Composition of the two (BCLO & CLWW) schemes does #decrease attack accuracy but is still far from providing acceptable security.
Exploiting known plaintexts is even easier.
Attacking frequency-hiding schemes - #Kerschbaum recently introduced a scheme that hides frequency information. However, a “#binomial” attack performs reasonably well, recovering on average 30% of first names and 7% of last names. Notably, it recovers majority of high-frequency plaintexts (despite not having frequency information leaked), suggesting these plaintexts are particularly poorly protected by any order-revealing scheme.
In terms of countermeasures, an obvious suggestion is to move towards less leaky schemes, such as those that only reveal order, including Kerschbaum's scheme and the more recent #Boneh et al. scheme based on #multilinear maps. Unfortunately in most settings there exists inherent #challenges to deployment of these schemes. Kerschbaum's scheme is relatively efficient, but requires client-side state which impedes scaling. The Boneh et al. scheme has ciphertexts larger by 10 orders of magnitude than BCLO ciphertexts and requires tens of minutes to compute encryptions.
#ope #bclo #ore #clww #bipartite #composition #decrease #kerschbaum #binomial #boneh #multilinear #challenges #encryption #quantumcomputing #quantumcryptography
Some thoughts on deviance residuals for the beta-Binomial distribution https://poissonconsulting.github.io/extras/articles/beta-binomial-deviance-residuals.html
#rstats #deviance #binomial #beta