Smart home applications aim to increase convenience, yet often require authentication to protect sensitive data. This is non-trivial: effortful authentication contradicts intended convenience, the multitude of devices raises scalability issues, many devices lack suitable interfaces, and the presence of other inhabitants requires intentional and acceptable interactions. To address these issues, we explored new and creative authentication interactions with an interaction relabeling approach using everyday objects. We conducted six focus group workshops with 20 participants in a living room and a kitchen setting that resulted in a variety of creative authentication interactions with analogue and digital objects. Furthermore, participants created authentication interactions based on tasks that they have to or wish to perform anyway such as cleaning the kitchen—thus primary tasks. This led us to explore the option to transform authentication from being an additional, secondary task toward using primary tasks further in an online study with 194 participants. Relevant implications in terms of acceptable authentication task characteristics, user perceptions, arising security challenges, and psychological habit research are discussed.

Fallback authentication, the process of re-establishing access to an account when the primary authenticator is unavailable, holds critical significance. Approaches range from secondary channels like email and SMS to personal knowledge questions (PKQs) and social authentication. A key difference to primary authentication is that the duration between enrollment and authentication can be much longer, typically months or years. However, few systems have been studied over extended timeframes, making it difficult to know how well these systems truly help users recover their accounts. We also lack meaningful comparisons of schemes as most prior work examined two mechanisms at most. We report the results of a long-term user study of the usability of fallback authentication over 18 months to provide a fair comparison of the four most commonly used fallback authentication methods. We show that users prefer email and SMS-based methods, while mechanisms based on PKQs and trustees lag regarding successful resets and convenience.

Login notifications intend to inform users about sign-ins and help them protect their accounts from unauthorized access. Notifications are usually sent if a login deviates from previous ones, potentially indicating malicious activity. They contain information like the location, date, time, and device used to sign in. Users are challenged to verify whether they recognize the login (because it was them or someone they know) or to protect their account from unwanted access. In a user study, we explore users’ comprehension, reactions, and expectations of login notifications. We utilize two treatments to measure users’ behavior in response to notifications sent for a login they initiated or based on a malicious actor relying on statistical sign-in information. We find that users identify legitimate logins but need more support to halt malicious sign-ins. We discuss the identified problems and give recommendations for service providers to ensure usable and secure logins for everyone.

AI-generated media has become a threat to our digital society as we know it. Forgeries can be created automatically and on a large scale based on publicly available technologies. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to these technological advances, the human perception of generated media has not been thoroughly studied yet.In this paper, we aim to close this research gap. We conduct the first comprehensive survey on people’s ability to detect generated media, spanning three countries (USA, Germany, and China), with 3,002 participants covering audio, image, and text media. Our results indicate that state-of-the-art forgeries are almost indistinguishable from "real" media, with the majority of participants simply guessing when asked to rate them as human- or machine-generated. In addition, AI-generated media is rated as more likely to be human-generated across all media types and all countries. To further understand which factors influence people’s ability to detect AI-generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research. In a regression analysis, we found that generalized trust, cognitive reflection, and self-reported familiarity with deepfakes significantly influence participants’ decisions across all media categories.

This paper investigates the digital security experiences of four at-risk user groups in Germany, including older adults (70+), teenagers (14-17), people with migration backgrounds, and people with low formal education. Using computer-assisted telephone interviews, we sampled 250 participants per group, representative of region, gender, and partly age distributions. We examine their device usage, concerns, prior negative incidents, perceptions of potential attackers, and information sources. Our study provides the first quantitative and nationally representative insights into the digital security experiences of these four at-risk groups in Germany. Our findings show that participants with migration backgrounds used the most devices, sought more security information, and reported more experiences with cybercrime incidents than other groups. Older adults used the fewest devices and were least affected by cybercrimes. All groups relied on friends and family and online news as their primary sources of security information, with little concern about their social circles being potential attackers. We highlight the nuanced differences between the four at-risk groups and compare them to the broader German population when possible. We conclude by presenting recommendations for education, policy, and future research aimed at addressing the digital security needs of these at-risk user groups.

Misconceptions about digital security and privacy topics in the general public frequently lead to insecure behavior. However, little is known about the prevalence and extent of such misconceptions in a global context. In this work, we present the results of the first large-scale survey of a global population on misconceptions: We conducted an online survey with n = 12, 351 participants in 12 countries on four continents. By investigating influencing factors of misconceptions around eight common security and privacy topics (including E2EE, Wi-Fi, VPN, and malware), we find the country of residence to be the strongest estimate for holding misconceptions. We also identify differences between non-Western and Western countries, demonstrating the need for region-specific research on user security knowledge, perceptions, and behavior. While we did not observe many outright misconceptions, we did identify a lack of understanding and uncertainty about several fundamental privacy and security topics.

The COVID-19 pandemic has prompted countries around the world to introduce smartphone apps to support disease control efforts. Their purposes range from digital contact tracing to quarantine enforcement to vaccination passports, and their effectiveness often depends on widespread adoption. While previous work has identified factors that promote or hinder adoption, it has typically examined data collected at a single point in time or focused exclusively on digital contact tracing apps. In this work, we conduct the first representative study that examines changes in people's attitudes towards COVID-19-related smartphone apps for five different purposes over the first 1.5 years of the pandemic. In three survey rounds conducted between Summer 2020 and Summer 2021 in the United States and Germany, with approximately 1,000 participants per round and country, we investigate people's willingness to use such apps, their perceived utility, and people's attitudes towards them in different stages of the pandemic. Our results indicate that privacy is a consistent concern for participants, even in a public health crisis, and the collection of identity-related data significantly decreases acceptance of COVID-19 apps. Trust in authorities is essential to increase confidence in government-backed apps and foster citizens' willingness to contribute to crisis management. There is a need for continuous communication with app users to emphasize the benefits of health crisis apps both for individuals and society, thus counteracting decreasing willingness to use them and perceived usefulness as the pandemic evolves.

Abstract Digital tools play an important role in fighting the current global COVID-19 pandemic. We conducted a representative online study in Germany on a sample of 599 participants to evaluate the user perception of vaccination certificates. We investigated five different variants of vaccination certificates based on deployed and planned designs in a between-group design, including paper-based and app-based variants. Our main results show that the willingness to use and adopt vaccination certificates is generally high. Overall, paper-based vaccination certificates were favored over app-based solutions. The willingness to use digital apps decreased significantly by a higher disposition to privacy and increased by higher worries about the pandemic and acceptance of the coronavirus vaccination. Vaccination certificates resemble an interesting use case for studying privacy perceptions for health-related data. We hope that our work will educate the currently ongoing design of vaccination certificates, give us deeper insights into the privacy of health-related data and apps, and prepare us for future potential applications of vaccination certificates and health apps in general.

The COVID-19 pandemic has fueled the development of smartphone applications to assist disease management. Many “corona apps” require widespread adoption to be effective, which has sparked public debates about the privacy, security, and societal implications of government-backed health applications. We conducted a representative online study in Germany (n = 1003), the US (n = 1003), and China (n = 1019) to investigate user acceptance of corona apps, using a vignette design based on the contextual integrity framework. We explored apps for contact tracing, symptom checks, quarantine enforcement, health certificates, and mere information. Our results provide insights into data processing practices that foster adoption and reveal significant differences between countries, with user acceptance being highest in China and lowest in the US. Chinese participants prefer the collection of personalized data, while German and US participants favor anonymity. Across countries, contact tracing is viewed more positively than quarantine enforcement, and technical malfunctions negatively impact user acceptance.

Traffic analysis attacks against the Tor network are a persisting threat to the anonymity of its users. The technical capabilities of attacks against encrypted Internet traffic have come a long way. Although the current state-of-the-art predicts high precision and accuracy for website fingerprinting and end-to-end confirmation, the concepts of these attacks often solely focus on their technical capabilities and ignore the operational requirements that are mandatory to get access to transmissions. In this work, we introduce three novel stepping-stone attacks that enable an adversary to (i) gain additional information about monitored connections, (ii) manipulate the Tor connection build-up, and (iii) conduct a targeted Denial-of-Service attack within the Tor infrastructure. All attacks exploit core defensive features of Tor and, consequently, are hard to patch. At the same time, our attacks are in line with standard attacker models for traffic analysis attacks. We demonstrate the feasibility of all three attacks in simulations and empirical case studies and emphasize their pivotal role in preparing a realistic setting for end-to-end confirmation attacks.

Abstract Over the past decade, research has explored managing the availability of shared personal online data, with particular focus on longitudinal aspects of privacy. Yet, there is no taxonomy that takes user perspective and technical approaches into account. In this work, we systematize research on longitudinal privacy management of publicly shared personal online data from these two perspectives: user studies capturing users’ interactions related to the availability of their online data and technical proposals limiting the availability of data. Following a systematic approach, we derive conflicts between these two sides that have not yet been addressed appropriately, resulting in a list of challenging open problems to be tackled by future research. While limitations of data availability in proposed approaches and real systems are mostly time-based, users’ desired models are rather complex, taking into account content, audience, and the context in which data has been shared. Our systematic evaluation reveals interesting challenges broadly categorized by expiration conditions, data co-ownership, user awareness, and security and trust.

In this article, we provide the first comprehensive study of user-chosen four- and six-digit PINs ( n =1705) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using six-digit PINs instead of four-digit PINs provides little to no increase in security and surprisingly may even decrease security. We also study the effects of blocklists, where a set of “easy to guess” PINs is disallowed during selection. Two such blocklists are in use today by iOS, for four digits (274 PINs) as well as six digits (2,910 PINs). We extracted both blocklists and compared them with six other blocklists, three for each PIN length. In each case, we had a small (four-digit: 27 PINs; six-digit: 29 PINs), a large (four-digit: 2,740 PINs; six-digit: 291,000 PINs), and a placebo blocklist that always excluded the first-choice PIN. For four-digit PINs, we find that the relatively small blocklist in use today by iOS offers little to no benefit against a throttled guessing attack. Security gains are only observed when the blocklist is much larger. In the six-digit case, we were able to reach a similar security level with a smaller blocklist. As the user frustration increases with the blocklists size, developers should employ a blocklist that is as small as possible while ensuring the desired security. Based on our analysis, we recommend that for four-digit PINs a blocklist should contain the 1,000 most popular PINs to provide the best balance between usability and security and for six-digit PINs the 2,000 most popular PINs should be blocked.

Risk-based Authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional features during login, and when observed feature values differ significantly from previously seen ones, users have to provide additional authentication factors such as a verification code. RBA has the potential to offer more usable authentication, but the usability and the security perceptions of RBA are not studied well.

We provide the first comprehensive study of user-chosen 4- and 6-digit PINs (n = 1220) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using 6-digit PINs instead of 4-digit PINs provides little to no increase in security, and surprisingly may even decrease security. We also study the effects of blacklists, where a set of "easy to guess" PINs is disallowed during selection. Two such blacklists are in use today by iOS, for 4-digits (274 PINs) as well as 6-digits (2910 PINs). We extracted both blacklists compared them with four other blacklists, including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN. We find that relatively small blacklists in use today by iOS offer little or no benefit against a throttled guessing attack. Security gains are only observed when the blacklists are much larger, which in turn comes at the cost of increased user frustration. Our analysis suggests that a blacklist at about 10 % of the PIN space may provide the best balance between usability and security.

Password strength meters are an important tool to help users choose secure passwords. Strength meters can only then provide reasonable guidance when they are accurate, i.e., their score correctly reflect password strength. A strength meter with low accuracy may do more harm than good and guide the user to choose passwords with a high score but low actual security. While a substantial number of different strength meters is proposed in the literature and deployed in practice, we are lacking a clear picture of which strength meters provide high accuracy, and thus are most helpful for guiding users. Furthermore, we lack a clear understanding of how to compare accuracies of strength meters. In this work, (i) we propose a set of properties that a strength meter needs to fulfill to be considered to have high accuracy, (ii) we use these properties to select a suitable measure that can determine the accuracy of strength meters, and (iii) we use the selected measure to compare a wide range of strength meters proposed in the academic literature, provided by password managers, operating systems, and those used on websites. We expect our work to be helpful in the selection of good password strength meters by service operators, and to aid the further development of improved strength meters.

Password reuse is widespread, so a breach of one provider's password database threatens accounts on other providers. When companies find stolen credentials on the black market and notice potential password reuse, they may require a password reset and send affected users a notification. Through two user studies, we provide insight into such notifications. In Study 1, 180 respondents saw one of six representative notifications used by companies in situations potentially involving password reuse. Respondents answered questions about their reactions and understanding of the situation. Notifications differed in the concern they elicited and intended actions they inspired. Concerningly, less than a third of respondents reported intentions to change any passwords. In Study 2, 588 respondents saw one of 15 variations on a model notification synthesizing results from Study 1. While the variations' impact differed in small ways, respondents' intended actions across all notifications would leave them vulnerable to future password-reuse attacks. We discuss best practices for password-reuse notifications and how notifications alone appear insufficient in solving password reuse.

Password vaults are used to store login credentials, usually encrypted by a master password, relieving the user from memorizing a large number of complex passwords. To manage accounts on multiple devices, vaults are often stored at an online service, which substantially increases the risk of leaking the (encrypted) vault. To protect the master password against guessing attacks, previous work has introduced cracking-resistant password vaults based on Honey Encryption. If decryption is attempted with a wrong master password, they output plausible-looking decoy vaults, thus seemingly disabling offline guessing attacks. In this work, we propose attacks against cracking-resistant password vaults that are able to distinguish between real and decoy vaults with high accuracy and thus circumvent the offered protection. These attacks are based on differences in the generated distribution of passwords, which are measured using Kullback-Leibler divergence. Our attack is able to rank the correct vault into the 1.3% most likely vaults (on median), compared to 37.8% of the best-reported attack in previous work. (Note that smaller ranks are better, and 50% is achievable by random guessing.) We demonstrate that this attack is, to a certain extent, a fundamental problem with all static Natural Language Encoders (NLE), where the distribution of decoy vaults is fixed. We propose the notion of adaptive NLEs and demonstrate that they substantially limit the effectiveness of such attacks. We give one example of an adaptive NLE based on Markov models and show that the attack is only able to rank the decoy vaults with a median rank of 35.1%.

Once data is released to the Internet, there is little hope to successfully delete it, as it may have been duplicated, reposted, and archived in multiple places. This poses a significant threat to users' privacy and their right to permanently erase their very own data. One approach to control the implications on privacy is to assign a lifetime value to the published data and ensure that the data is no longer accessible after this point in time. However, such an approach suffers from the inability to successfully predict the right time when the data should vanish. Consequently, the author of the data can only estimate the correct time, which unfortunately can cause the premature or belated deletion of data.

Graphical passwords were proposed as an alternative to overcome the inherent limitations of text-based passwords, inspired by research that shows that the graphical memory of humans is particularly well developed. A graphical password scheme that has been widely adopted is the Android Unlock Pattern, a special case of the Pass-Go scheme with grid size restricted to 3x3 points and restricted stroke count.

Password-based authentication is widely used today, despite problems with security and usability. To control the negative effects of some of these problems, best practice mandates that servers do not store passwords in clear, but password hashes are used. Password hashes slow down the password verification and thus the rate of password guessing in the event of a server compromise. A slower password hash is more secure, as the attacker needs more resources to test password guesses, but at the same time it slows down password verification for the legitimate server. This puts a practical limit on the hardness of the password hash and thus the security of password storage.

We show that the amount of information about the key that an unknown-message attacker can extract from a deterministic side-channel is bounded from above by |O| log (n+1) bits, where n is the number of side-channel measurements and O is the set of possible observations. We use this bound to derive a novel countermeasure against timing attacks, where the strength of the security guarantee can be freely traded for the resulting performance penalty. We give algorithms that efficiently and optimally adjust this trade-off for given constraints on the side-channel leakage or on the efficiency of the cryptosystem. Finally, we perform a case-study that shows that applying our countermeasure leads to implementations with minor performance overhead and formal security guarantees.

Reflecting objects such as tea pots and glasses, but also diffusely reflecting objects such as a user's shirt, can be used to spy on confidential data displayed on a monitor. First, we show how reflections in the user's eye can be exploited for spying on confidential data. Second, we investigate to what extent monitor images can be reconstructed from the diffuse reflections on a wall or the user's clothes, and provide information-theoretic bounds limiting this type of attack. Third, we evaluate the effectiveness of several countermeasures. This substantially improves previous work (Backes et al., IEEE Symposium on Security & Privacy, 2008).

We present a novel eavesdropping technique for spying at a distance on data that is displayed on an arbitrary computer screen, including the currently prevalent LCD monitors. Our technique exploits reflections of the screen's optical emanations in various objects that one commonly finds in close proximity to the screen and uses those reflections to recover the original screen content. Such objects include eyeglasses, tea pots, spoons, plastic bottles, and even the eye of the user. We have demonstrated that this attack can be successfully mounted to spy on even small fonts using inexpensive, off-the-shelf equipment (less than 1500 dollars) from a distance of up to 10 meters. Relying on more expensive equipment allowed us to conduct this attack from over 30 meters away, demonstrating that similar attacks are feasible from the other side of the street or from a close-by building. We additionally establish theoretical limitations of the attack; these limitations may help to estimate the risk that this attack can be successfully mounted in a given environment.

We investigate a new type of information flow in the electronic publishing process. We show that the use of PostScript in this process introduces serious confidentiality issues. In particular, we explain how the reviewer's anonymity in the peer-reviewing process can be compromised by maliciously prepared PostScript documents. A demonstration of this attack is available. We briefly discuss how this attack can be extended to other document formats as well.

We present the first cryptographically sound Dolev-Yao-style security proof of a comprehensive electronic payment system. The payment system is a slightly simplified variant of the 3KP payment system and comprises a variety of different security requirements ranging from basic ones like the impossibility of unauthorized payments to more sophisticated properties like disputability. We show that the payment system is secure against arbitrary active attacks, including arbitrary concurrent protocol runs and arbitrary manipulation of bitstrings within polynomial time if the protocol is implemented using provably secure cryptographic primitives. Although we achieve security under cryptographic definitions, our proof does not have to deal with probabilistic aspects of cryptography and is hence within the scope of current proof tools. The reason is that we exploit a recently proposed Dolev-Yao-style cryptographic library with a provably secure cryptographic implementation. Together with composition and preservation theorems of the underlying model, this allows us to perform the actual proof effort in a deterministic setting corresponding to a slightly extended Dolev-Yao model.