To prevent credential stuffing attacks, industry best practice now proactively checks if user credentials are present in known data breaches. Recently, some web services, such as HaveIBeenPwned (HIBP) and Google Password Checkup (GPC), have started providing APIs to check for breached passwords. We refer to such services as compromised credential checking (C3) services. We give the first formal description of C3 services, detailing different settings and operational requirements, and we give relevant threat models.One key security requirement is the secrecy of a user's passwords that are being checked. Current widely deployed C3 services have the user share a small prefix of a hash computed over the user's password. We provide a framework for empirically analyzing the leakage of such protocols, showing that in some contexts knowing the hash prefixes leads to a 12x increase in the efficacy of remote guessing attacks. We propose two new protocols that provide stronger protection for users' passwords, implement them, and show experimentally that they remain practical to deploy.

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Given that a MAC address can uniquely identify a person or a vehicle, continuous tracking over a large geographical scale has raised serious privacy concerns amongst governments and the general public. Prior work has demonstrated that simple hash-based approaches to anonymization can be easily inverted due to the small search space of MAC addresses. In particular, it is possible to represent the entire allocated MAC address space in 39 bits and that frequency-based attacks allow for 50% of MAC addresses to be enumerated in 31 bits. We present a practical approach to MAC address anonymization using both computationally expensive hash functions and truncating the resulting hashes to allow for k-anonymity. We provide an expression for computing the percentage of expected collisions, demonstrating that for digests of 24 bits it is possible to store up to 168, 617 MAC addresses with the rate of collisions less than 1%. We experimentally demonstrate that a rate of collision of 1% or less can be achieved by storing data sets of 100 MAC addresses in 13 bits, 1, 000 MAC addresses in 17 bits and 10, 000 MAC addresses in 20 bits. a https://orcid.

During the COVID-19 (SARS-CoV-2) epidemic, Contact Tracing emerged as an essential tool for managing the epidemic. App-based solutions have emerged for Contact Tracing, including a protocol designed by Apple and Google (influenced by an open-source protocol known as DP3T). This protocol contains two well-documented de-anonymisation attacks. Firstly that when someone is marked as having tested positive and their keys are made public, they can be tracked over a large geographic area for 24 hours at a time. Secondly, whilst the app requires a minimum exposure duration to register a contact, there is no cryptographic guarantee for this property. This means an adversary can scan Bluetooth networks and retrospectively find who is infected. We propose a novel "cross hashing" approach to cryptographically guarantee minimum exposure durations. We further mitigate the 24-hour data exposure of infected individuals and reduce computational time for identifying if a user has been exposed using k-Anonymous buckets of hashes and Private Set Intersection. We empirically demonstrate that this modified protocol can offer like-for-like efficacy to the existing protocol.

Password authentication is an essential and widespread form of user authentication on the Internet with no other authentication system matching its dominance. When a password on one website is breached, if reused, the stolen password can be used to gain access to multiple other authenticated websites. Even amongst technically educated users, the security issues surrounding password reuse are not well understood and restrictive password composition rules have been unsuccessful in reducing password reuse. In response, the US NIST have published standards outlining that, when setting passwords, authentication systems should validate that user passwords have not already been compromised or breached. We propose a mechanism to allows for clients to anonymously validate whether or not a password has been identified in a compromised database, without needing to download the entire database or send their password to a third-party service. A mechanism is proposed whereby password hash data is generalized such that it holds the k-anonymity property. An implementation is constructed to identify to what extent the data should be generalized for it to hold k-anonymity and additionally to group password hashes by their generalized anonymous value. The implementation is run on a database of over 320 million leaked passwords and the results of the anonymization process are considered. Password authentication is an essential and widespread form of user authentication on the Internet with no other authentication system matching its dominance. When a password on one website is breached, if reused, the stolen password can be used to gain access to multiple other authenticated websites. Even amongst technically educated users, the security issues surrounding password reuse are not well understood and restrictive password composition rules have been unsuccessful in reducing password reuse. In response, the US NIST have published standards outlining that, when setting passwords, authentication systems should validate that user passwords have not already been compromised or breached. We propose a mechanism to allows for clients to anonymously validate whether or not a password has been identified in a compromised database, without needing to download the entire database or send their password to a third-party service. A mechanism is proposed whereby password hash data is generalized such that it holds the k-anonymity property. An implementation is constructed to identify to what extent the data should be generalized for it to hold k-anonymity and additionally to group password hashes by their generalized anonymous value. The implementation is run on a database of over 320 million leaked passwords and the results of the anonymization process are considered.