Joseph Bonneau

The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes

We evaluate two decades of proposals to replace text passwords for general-purpose user authentication on the web using a broad set of twenty-five usability, deployability and security benefits that an ideal scheme might provide. The scope of proposals we survey is also extensive, including password management software, federated login protocols, graphical password schemes, cognitive authentication schemes, one-time passwords, hardware tokens, phone-aided schemes and biometrics. Our comprehensive approach leads to key insights about the difficulty of replacing passwords. Not only does no known scheme come close to providing all desired benefits: none even retains the full set of benefits that legacy passwords already provide. In particular, there is a wide range from schemes offering minor security benefits beyond legacy passwords, to those offering significant security benefits in return for being more costly to deploy or more difficult to use. We conclude that many academic proposals have failed to gain traction because researchers rarely consider a sufficiently wide range of real-world constraints. Beyond our analysis of current schemes, our framework provides an evaluation methodology and benchmark for future web authentication proposals.

The Science of Guessing: Analyzing an Anonymized Corpus of 70 Million Passwords

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 attackers 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.

The Privacy Jungle:On the Market for Data Protection in Social Networks

We have conducted the first thorough analysis of the market for privacy practices and policies in online social networks. From an evaluation of 45 social networking sites using 260 criteria we find that many popular assumptions regarding privacy and social networking need to be revisited when considering the entire ecosystem instead of only a handful of well-known sites. Contrary to the common perception of an oligopolistic market, we find evidence of vigorous competition for new users. Despite observing many poor security practices, there is evidence that social network providers are making efforts to implement privacy enhancing technologies with substantial diversity in the amount of privacy control offered. However, privacy is rarely used as a selling point, even then only as auxiliary, nondecisive feature. Sites also failed to promote their existing privacy controls within the site. We similarly found great diversity in the length and content of formal privacy policies, but found an opposite promotional trend: though almost all policies are not accessible to ordinary users due to obfuscating legal jargon, they conspicuously vaunt the sites’ privacy practices. We conclude that the market for privacy in social networks is dysfunctional in that there is significant variation in sites’ privacy controls, data collection requirements, and legal privacy policies, but this is not effectively conveyed to users. Our empirical findings motivate us to introduce the novel model of a privacy communication game, where the economically rational choice for a site operator is to make privacy control available to evade criticism from privacy fundamentalists, while hiding the privacy control interface and privacy policy to maximize sign-up numbers and encourage data sharing from the pragmatic majority of users.

A Birthday Present Every Eleven Wallets? The Security of Customer-Chosen Banking PINs

We provide the first published estimates of the difficulty of guessing a human-chosen 4-digit PIN. We begin with two large sets of 4-digit sequences chosen outside banking for online passwords and smartphone unlock-codes. We use a regression model to identify a small number of dominant factors influencing user choice. Using this model and a survey of over 1,100 banking customers, we estimate the distribution of banking PINs as well as the frequency of security-relevant behaviour such as sharing and reusing PINs. We find that guessing PINs based on the victims’ birthday, which nearly all users carry documentation of, will enable a competent thief to gain use of an ATM card once for every 11–18 stolen wallets, depending on whether banks prohibit weak PINs such as 1234. The lesson for cardholders is to never use one’s date of birth as a PIN. The lesson for card-issuing banks is to implement a denied PIN list, which several large banks still fail to do. However, blacklists cannot effectively mitigate guessing given a known birth date, suggesting banks should move away from customer-chosen banking PINs in the long term.

Mixcoin: Anonymity for Bitcoin with Accountable Mixes

We propose Mixcoin, a protocol to facilitate anonymous payments in Bitcoin and similar cryptocurrencies. We build on the emergent phenomenon of currency mixes, adding an accountability mechanism to expose theft. We demonstrate that incentives of mixes and clients can be aligned to ensure that rational mixes will not steal. Our scheme is efficient and fully compatible with Bitcoin. Against a passive attacker, our scheme provides an anonymity set of all other users mixing coins contemporaneously. This is an interesting new property with no clear analog in better-studied communication mixes. Against active attackers our scheme offers similar anonymity to traditional communication mixes.

Privacy suites: shared privacy for social networks

Creating privacy controls for social networks that are both expressive and usable is a major challenge. Lack of user understanding of privacy settings can lead to unwanted disclosure of private information and, in some cases, to material harm. We propose a new paradigm which allows users to easily choose “suites” of privacy settings which have been specified by friends or trusted experts, only modifying them if they wish. Given that most users currently stick with their default, operator-chosen settings, such a system could dramatically increase the privacy protection that most users experience with minimal time investment.

Passwords and the evolution of imperfect authentication

Theory on passwords has lagged practice, where large providers use back-end smarts to survive with imperfect technology.

Linguistic properties of multi-word passphrases

We examine patterns of human choice in a passphrase-based authentication system deployed by Amazon, a large online merchant. We tested the availability of a large corpus of over 100,000 possible phrases at Amazons registration page, which prohibits using any phrase already registered by another user. A number of large, readily-available lists such as movie and book titles prove effective in guessing attacks, suggesting that passphrases are vulnerable to dictionary attacks like all schemes involving human choice. Extending our analysis with natural language phrases extracted from linguistic corpora, we find that phrase selection is far from random, with users strongly preferring simple noun bigrams which are common in natural language. The distribution of chosen passphrases is less skewed than the distribution of bigrams in English text, indicating that some users have attempted to choose phrases randomly. Still, the distribution of bigrams in natural language is not nearly random enough to resist offline guessing, nor are longer three- or four-word phrases for which we see rapidly diminishing returns.

Cracking-Resistant Password Vaults Using Natural Language Encoders

Password vaults are increasingly popular applications that store multiple passwords encrypted under a single master password that the user memorizes. A password vault can greatly reduce the burden on a user of remembering passwords, but introduces a single point of failure. An attacker that obtains a users encrypted vault can mount offline brute-force attacks and, if successful, compromise all of the passwords in the vault. In this paper, we investigate the construction of encrypted vaults that resist such offline cracking attacks and force attackers instead to mount online attacks. Our contributions are as follows. We present an attack and supporting analysis showing that a previous design for cracking-resistant vaults -- the only one of which we are aware -- actually degrades security relative to conventional password-based approaches. We then introduce a new type of secure encoding scheme that we call a natural language encoder (NLE). An NLE permits the construction of vaults which, when decrypted with the wrong master password, produce plausible-looking decoy passwords. We show how to build NLEs using existing tools from natural language processing, such as n-gram models and probabilistic context-free grammars, and evaluate their ability to generate plausible decoys. Finally, we present, implement, and evaluate a full, NLE-based cracking-resistant vault system called NoCrack.

Incentive Compatibility of Bitcoin Mining Pool Reward Functions

In this paper we introduce a game-theoretic model for reward functions in Bitcoin mining pools. Our model consists only of an unordered history of reported shares and gives participating miners the strategy choices of either reporting or delaying when they discover a share or full solution. We defined a precise condition for incentive compatibility to ensure miners strategy choices optimize the welfare of the pool as a whole. With this definition we show that proportional mining rewards are not incentive compatible in this model. We introduce and analyze a novel reward function which is incentive compatible in this model. Finally we show that the popular reward function pay-per-last-N-shares is also incentive compatible in a more general model.