It is practically impossible for users to memorize a large portfolio of strong and individual passwords for their online accounts. A solution is to generate passwords randomly and store them. Yet, storing passwords instead of memorizing them bears the risk of loss, e.g., in situations where the device on which the passwords are stored is damaged, lost, or stolen. This makes the creation of backups of the passwords indispensable. However, placing such backups at secure locations to protect them as well from loss and unauthorized access and keeping them up-to-date at the same time is an unsolved problem in practice. We present PASCO, a backup solution for passwords that solves this challenge. PASCO backups need not to be updated, even when the user's password portfolio is changed. PASCO backups can be revoked without having physical access to them. This prevents password leakage, even when a user loses control over a backup. Additionally, we show how to extend PASCO to enable a fully controllable emergency access. It allows a user to give someone else access to his passwords in urgent situations. We also present a security evaluation and an implementation of PASCO.
In 2007, Flor ^ e ncio and Herley @cite_26 reported that users on average have 25 accounts. Due to the growth in online services, the number has strongly increased over the last ten years, which makes the memorization of secure passwords for all accounts practically impossible. Studies have shown that users typically cope with this challenge by selecting passwords that are easy to remember and reuse passwords across accounts @cite_51 @cite_8 @cite_52 @cite_6 @cite_48 @cite_9 . This makes the passwords vulnerable to various attacks such as brute-force @cite_22 @cite_1 @cite_23 , dictionary @cite_43 @cite_47 , and social engineering @cite_21 .
@cite_26 @cite_22 @cite_8 @cite_48 @cite_9 @cite_21 @cite_1 @cite_52 @cite_6 @cite_43 @cite_23 @cite_47 @cite_51
2048755632 2073342447 2145881505 2030993695
We report on the largest corpus of user-chosen passwords ever studied, consisting of anonymized password histograms representing almost 70 million Yahoo! users, mitigating privacy concerns while enabling analysis of dozens of subpopulations based on demographic factors and site usage characteristics. This large data set motivates a thorough statistical treatment of estimating guessing difficulty by sampling from a secret distribution. In place of previously used metrics such as Shannon entropy and guessing entropy, which cannot be estimated with any realistically sized sample, we develop partial guessing metrics including a new variant of guesswork parameterized by an attacker's desired success rate. Our new metric is comparatively easy to approximate and directly relevant for security engineering. By comparing password distributions with a uniform distribution which would provide equivalent security against different forms of guessing attack, we estimate that passwords provide fewer than 10 bits of security against an online, trawling attack, and only about 20 bits of security against an optimal offline dictionary attack. We find surprisingly little variation in guessing difficulty; every identifiable group of users generated a comparably weak password distribution. Security motivations such as the registration of a payment card have no greater impact than demographic factors such as age and nationality. Even proactive efforts to nudge users towards better password choices with graphical feedback make little difference. More surprisingly, even seemingly distant language communities choose the same weak passwords and an attacker never gains more than a factor of 2 efficiency gain by switching from the globally optimal dictionary to a population-specific lists. Today's Internet services rely heavily on text-based passwords for user authentication. The pervasiveness of these services coupled with the difficulty of remembering large numbers of secure passwords tempts users to reuse passwords at multiple sites. In this paper, we investigate for the first time how an attacker can leverage a known password from one site to more easily guess that user's password at other sites. We study several hundred thousand leaked passwords from eleven web sites and conduct a user survey on password reuse; we estimate that 43- 51 of users reuse the same password across multiple sites. We further identify a few simple tricks users often employ to transform a basic password between sites which can be used by an attacker to make password guessing vastly easier. We develop the first cross-site password-guessing algorithm, which is able to guess 30 of transformed passwords within 100 attempts compared to just 14 for a standard password-guessing algorithm without cross-site password knowledge. The underlying issues relating to the usability and security of multiple passwords are largely unexplored. However, we know that people generally have difficulty remembering multiple passwords. This reduces security since users reuse the same password for different systems or reveal other passwords as they try to log in. We report on a laboratory study comparing recall of multiple text passwords with recall of multiple click-based graphical passwords. In a one-hour session (short-term), we found that participants in the graphical password condition coped significantly better than those in the text password condition. In particular, they made fewer errors when recalling their passwords, did not resort to creating passwords directly related to account names, and did not use similar passwords across multiple accounts. After two weeks, participants in the two conditions had recall success rates that were not statistically different from each other, but those with text passwords made more recall errors than participants with graphical passwords. In our study, click-based graphical passwords were significantly less susceptible to multiple password interference in the short-term, while having comparable usability to text passwords in most other respects. Computer users are asked to generate, keep secret, and recall an increasing number of passwords for uses including host accounts, email servers, e-commerce sites, and online financial services. Unfortunately, the password entropy that users can comfortably memorize seems insufficient to store unique, secure passwords for all these accounts, and it is likely to remain constant as the number of passwords (and the adversary's computational power) increases into the future. In this paper, we propose a technique that uses a strengthened cryptographic hash function to compute secure passwords for arbitrarily many accounts while requiring the user to memorize only a single short password. This mechanism functions entirely on the client; no server-side changes are needed. Unlike previous approaches, our design is both highly resistant to brute force attacks and nearly stateless, allowing users to retrieve their passwords from any location so long as they can execute our program and remember a short secret. This combination of security and convenience will, we believe, entice users to adopt our scheme. We discuss the construction of our algorithm in detail, compare its strengths and weaknesses to those of related approaches, and present Password Multiplier, an implementation in the form of an extension to the Mozilla Firefox web browser.