Practical Delegatable Anonymous Credentials From Equivalence Class Signatures

Anonymous credentials systems (ACs) are a powerful cryptographic tool for privacy-preserving applications and provide strong user privacy guarantees for authentication and access control. ACs allow users to prove possession of attributes encoded in a credential without revealing any information beyond them. A delegatable AC (DAC) system is an enhanced AC system that allows the owners of credentials to delegate the obtained credential to other users. This allows to model hierarchies as usually encountered within public-key infrastructures (PKIs). DACs also provide stronger privacy guarantees than traditional AC systems since the identities of issuers and delegators are also hidden. A credential issuer\u27s identity may convey information about a user\u27s identity even when all other information about the user is protected. We present a novel delegatable anonymous credential scheme that supports attributes, provides anonymity for delegations, allows the delegators to restrict further delegations, and also comes with an efficient construction. In particular, our DAC credentials do not grow with delegations, i.e., are of constant size. Our approach builds on a new primitive that we call structure-preserving signatures on equivalence classes on updatable commitments (SPSEQ-UC). The high-level idea is to use a special signature scheme that can sign vectors of set commitments which can be extended by additional set commitments. Signatures additionally include a user\u27s public key, which can be switched. This allows us to efficiently realize delegation in the DAC. Similar to conventional SPSEQ signatures, the signatures and messages can be publicly randomized and thus allow unlinkable showings in the DAC system. We present further optimizations such as cross-set commitment aggregation that, in combination, enable selective, efficient showings in the DAC without using costly zero-knowledge proofs. We present an efficient instantiation that is proven to be secure in the generic group model and finally demonstrate the practical efficiency of our DAC by presenting performance benchmarks based on an implementation

Towards an Open Source Toolkit for Ubiquitous Device Authentication

Most authentication protocols designed for ubiquitous computing environments try to solve the problem of intuitive, scalable, secure authentication of wireless communication. Due to the diversity of requirements, protocols tend to be implemented within specific research prototypes and can not be used easily in other applications. We propose to develop a common toolkit for ubiquitous device authentication to foster wide usability of research results. This paper outlines design goals and presents a first, freely available implementation

Adversary Models for Mobile Device Authentication

Publisher Copyright: © 2021 Copyright held by the owner/author(s).Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.Peer reviewe

A Large-Scale Data Collection and Evaluation Framework for Android Device Security Attributes

Android’s fast-lived development cycles and increasing amounts of manufacturers and device models make a comparison of relevant security attributes, in addition to the already difficult comparison of features, more challenging. Most smartphone reviews only consider offered features in their analysis. Smartphone manufacturers include their own software on top of the Android Open Source Project (AOSP) to improve user experience, to add their own pre-installed apps or apps from third-party sponsors, and to distinguish themselves from their competitors. These changes affect the security of smartphones. It is insufficient to validate device security state only based on measured data from real devices for a complete assessment. Promised major version releases, security updates, security update schedules of devices, and correct claims on security and privacy of pre-installed software are some aspects, which need statistically significant amounts of data to evaluate. Lack of software and security updates is a common reason for shorter lifespans of electronics, especially for smartphones. Validating the claims of manufacturers and publishing the results creates incentives towards more sustainable maintenance and longevity of smartphones. We present a novel scalable data collection and evaluation framework, which includes multiple sources of data like dedicated device farms, crowdsourcing, and webscraping. Our solution improves the comparability of devices based on their security attributes by providing measurements from real devices

Uraniborg's device preloaded app risks scoring metrics

The security of Android devices depends on a wide range of factors. In this paper we focus on quantifying the risks associated with one important factor: the security and privacy posture of preloaded apps. Such applications deserve particular attention since they are installed by the manufacturer on all devices of a particular make and model, individual apps may have elevated privileges beyond those available to apps installed via the Google Play Store, and typically cannot be removed by the user. In order to measure the risk presented by preloaded apps in a quantifiable way, we adopt a numerical approach and derive a single overall score for a given handset and therefore support the relative comparison of risks posed by different handsets. Due to the difficulty in computing the security and privacy risk, we approximate the actual risk by estimating the attack surface 1 presented by this layer of software. We therefore present an extensible mathematical software framework that allows us to define, compute, and analyze various aspects of security and privacy risks of preloaded Android apps in a systematic manner