Passwords are the de facto standard for online authentication today, and will likely remain so for the foreseeable future. As a consequence, the security community has extensively explored how users behave with passwords, producing recommendations for password policies that promote password security and usability for users. However, it is the website administrators who must adopt such recommendations to enact improvements to online authentication in practice. To date, there has been limited investigation of how web administrators manage password policies for their sites. To improve online authentication at scale, we must understand the factors behind this specific population’s behaviors and decisions, and how to help administrators deploy more secure password policies.In this paper, we explore how web administrators determine the password policies that they employ, what considerations impact a policy’s evolution, and what challenges administrators encounter when managing a site’s policy. To do so, we conduct an online survey and in-depth semi-structured interviews with 11 US-based web administrators with direct experience managing website password policies. Through our qualitative study, we identify a small set of key factors driving the majority of password policy decisions, and barriers that inhibit administrators from enacting policies that are more aligned with modern guidelines. Moving forward, we propose directions for future research and community action that may help administrators manage password policies more effectively.

Abstract In this paper, we propose a novel least significant bit embedding approach that capitalizes on the skewed distribution of letter and word frequencies to achieve higher image capacity, quality, and security. We initially conduct a study that involves all of the character frequencies using a data set of 14.245 billion characters. Huffman coding for each character is generated on the basis of its probability of occurrence. Furthermore, the top 100 000 most frequent words are transformed into a smaller ciphertext that has a lower cost. Our work demonstrates that recognizing characters and words on the basis of their frequency patterns and prioritizing them accordingly has a greater prospect of reducing the overall cost of embedding. The proposed scheme significantly outperforms Lempel–Ziv–Welch compression with an average of 45% fewer embedded bits. Moreover, the image quality is improved by a mean peak signal‐to‐noise ratio value of 6.9%. The proposed method also establishes a security embedding by proposing a novel shuffling algorithm. Copyright © 2017 John Wiley & Sons, Ltd.