We study passwords from the perspective of how they are generated, with the goal of better understanding how to distinguish good passwords from bad ones. Based on reviews of large quantities of passwords, we argue that users produce passwords using a small set of rules and types of components, both of which we describe herein. We build a parser of passwords, and show how this can be used to gain a better understanding of passwords, as well as to block weak passwords.
The world has already witnessed many epidemic
diseases in the past years, like H1N1, SARS, and Ebola etc.
Now, Covid-19 has also been added to list, which is declared
as pandemic by World Health Organization. One of the most
commonly used method to tackle the spread of such diseases
is using mobile applications to perform contact tracing of the
infected person. However, contact tracing applications involve
transmitting sensitive location based data of the infected person
to the government servers. Therefore, recently this has raised a lot
of concerns regarding privacy of the infected persons. This work
proposes a light-weight and secure encryption scheme, based on
location based encryption which can be used to transfer the
location data to the server without compromising its security.
The main aim of the work is design an algorithm in such a way
that the encrypted transferred data can only be decrypted at
the server and in-between data leakage can be prevented. This
work proposes to use location based encryption combined with
Learning with Errors problems in Lattices, which can provide a
solution to privacy concerns in contact tracing, which will even
be applicable in the post quantum period.
Abstract-We describe a common but poorly known type of fraud -so-called liar buyer fraud -and explain why traditional anti-fraud technology has failed to curb this problem. We then introduce a counter-intuitive technique based on user interface modification to address liar-buyer fraud, and report result of experiments supporting that our technique has the potential of dramatically reducing fraud losses. We used a combination of role playing and questionnaires to determine the behavior and opinions of about 1700 subjects, and found that our proposed technique results in a statistically significant reduction of fraud rates for both men and women in an experimental setting. Our approach has not yet been tested on real e-commerce traffic, but appears sufficiently promising to do that. Our findings also support that men are more willing to lie and defraud than women are; but maybe more interestingly, our analysis shows that the technique we introduce make men as honest as women.
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