ICT Systems Security and Privacy Protection

Frontmatter

Web Content Integrity: Tamper-Proof Websites Beyond HTTPS

Abstract

We propose Web Content Integrity, a framework that allows a service provider to guarantee the integrity of their static website, even in the face of a compromised web server. Such integrity assurances can then be used to implement a secure end-to-end encryption application built in the form of a website. Our framework encompasses developers, the Domain Name System, and web browsers. To accomplish the integrity guarantees, our framework makes use of an index of queryable URLs and allowed redirects for the website, and publishes the cryptographic hash value of the index in the DNS. Web browsers can then use the information from the DNS to verify that the resources they retrieve from the web server have not been tampered with. The required data structures can be generated automatically, and the framework introduces an initial delay of about 4 ms and a recurring delay for each request of about 2 ms for a sample website.

Sven Zemanek, Sebastian Tauchert, Max Jens Ufer, Lilli Bruckschen

Privacy-Preserving Clustering for Multi-dimensional Data Randomization Under LDP

Abstract

Randomization of multi-dimensional data under local differential privacy is a significant and practical application of big data. Because of the dimensionality issues, most existing works suffer from low accuracy when estimating joint probability distributions. In this paper, a set of attributes is divided into smaller clusters where the attributes are associated in terms of their dependencies. A privacy-preserving algorithm is proposed to estimate the dependencies of an attribute without disclosing the private values in the multi-dimensional data. Local differential privacy is guaranteed in the scheme. Using the clusters of attributes, the joint probabilities for multi-dimensional data can be estimated efficiently using two building blocks, called RR-independent and RR-Ind-Joint schemes. The experiments using some open datasets demonstrate that the dependencies of attributes can be estimated accurately and that the proposed algorithm outperforms existing state-of-the-art schemes in cases where the dimensionality is high.

Hiroaki Kikuchi

Hierarchical Model-Based Cybersecurity Risk Assessment During System Design

Abstract

Cybersecurity risk assessment has become a critical priority in systems development and the operation of complex networked systems. However, current state-of-the-art approaches for detecting vulnerabilities, such as automated security testing or penetration testing, often result in late detections. Thus, there is a growing need for security by design, which involves conducting security-related analyses as early as possible in the system development life cycle. This paper proposes a novel hierarchical model-based security risk assessment approach that enables the early assessment of security risks during the system design process. The approach uses different OMG UML-based models, supplemented by a lightweight extension using profiles and stereotypes. Various security attributes, including vulnerability information and asset values, are then used by algorithms to compute relevant properties including threat space, possible attack paths, and selected network-based security metrics. A real-life industrial example is then used to demonstrate the approach.

Tino Jungebloud, Nhung H. Nguyen, Dong Seong Kim, Armin Zimmermann

The Influence of Privacy Concerns on Cryptocurrency Acceptance

Abstract

Despite the hype, cryptocurrencies have to far failed to establish themselves as a means of payment for everyday transactions, spawning a wealth of research into acceptance factors and obstacles for cryptocurrency adoption. Our paper adds to this literature by investigating the role of organizational privacy concerns and risk perceptions on cryptocurrency acceptance. Employing a representative survey of German e-commerce users with 257 respondents we find that while risk perceptions and concerns about data collection do affect adoption willingness for cryptocurrencies, neither are useful for predicting actual adoption behavior. This is especially notable since the lack of central counterparties that may steal funds or personal data was one of the original motivations for the creation of the first cryptocurrencies. Our results provide insight into the nature of cryptocurrency adoption and highlights a discrepancy between intention and behavior.

Peter Hamm, Sebastian Pape, Kai Rannenberg

Automated Enrichment of Logical Attack Graphs via Formal Ontologies

Abstract

Attack graphs represent the possible actions of adversaries to attack a system. Cybersecurity experts use them to make decisions concerning remediation and recovery plans. There are different attack graph-building approaches. We focus on logical attack graphs. Networks and vulnerabilities constantly change; we propose an attack graph enrichment approach based on semantic augmentation post-processing of the logic predicates. Mapping attack graphs with alerts from a monitored system allows for confirming successful attack actions and updating according to network and vulnerability changes. The predicates get periodically updated based on attack evidence and ontology knowledge, allowing us to verify whether changes lead the attacker to the initial goals or cause further damage to the system not anticipated in the initial graphs. We illustrate our approach using a specific cyber-physical scenario affecting smart cities.

Kéren Saint-Hilaire, Frédéric Cuppens, Nora Cuppens, Joaquin Garcia-Alfaro

Detecting Web Bots via Mouse Dynamics and Communication Metadata

Abstract

The illegitimate automated usage of Internet services by web robots (bots) is an ongoing problem. While bots increase the cost of operations for service providers and can affect user satisfaction, e.g., in social media and games, the main problem is that some services should only be usable by humans, but their automated usage cannot be prevented easily. Currently, services are protected against bots using visual CAPTCHA systems, the de facto standard. However, they are often annoying for users to solve. Typically, CATPCHAs are combined with heuristics and machine-learning approaches to reduce the number of times a human needs to solve them. These approaches use request data like IP and cookies but also biometric data like mouse movements. Such detection systems are primarily closed source, do not provide any performance evaluation, or have unrealistic assumptions, e.g., that sophisticated bots only move the mouse in straight lines. Therefore we conducted an experiment to evaluate the usefulness of detection techniques based on mouse dynamics, request metadata, and a combination of both. Our findings indicate that biometric data in the form of mouse dynamics performs better than request data for bot detection. Further, training a mouse dynamic classifier benefits from external and not only website-specific mouse dynamics. Our classifier, which differentiates between artificial and human mouse movements, achieves similar results to related work under stricter and more realistic conditions.

August See, Tatjana Wingarz, Matz Radloff, Mathias Fischer

Practical Single-Round Secure Wildcard Pattern Matching

Abstract

Secure pattern matching allows a client who holds a substring (pattern) to find all the substring’s locations appearing in the long string (text) stored in a server. Meanwhile, the server should not learn any information about the pattern or the matching results. Wildcard pattern matching (WPM) problem, a specific variant with more realistic significance, defines that the pattern contains wildcards that can match any character in the text.

Previous studies introduce various approaches for the WPM problem but requires at least a two-round protocol or computation cost linear to input length. Oriented to applications in the client-server mode, however, existing solutions are not practical and efficient enough. Therefore we focus on the round and computation complexity of the WPM. In this paper, under the semi-honest model, we propose a single-round secure WPM protocol based on oblivious transfer (OT) and secret sharing schemes. The insight of our proposed protocol is the reduction from the WPM to the process of secret sharing and reconstruction in a novel way. We provide a customized OT construction and apply the OT extension technique to the protocol, where the client and the server need merely a constant number of public key operations in a round of communication. In addition, we prove the security of the protocol in the ideal/real simulation paradigm and evaluate the performance. Compared to existing secure WPM protocols, both theoretical and experimental results show that our protocol is more practical.

Jun Xu, Shengnan Zhao, Chuan Zhao, Zhenxiang Chen, Zhe Liu, Liming Fang

Efficient Non-interactive Anonymous Communication

Abstract

Methods for untraceable and anonymous communication, such as anonymous routing networks and dining cryptographers networks, are in general very complex and suffer from high performance overhead of a minimum order of \(N^2\) encryptions for N participants. In this paper, we propose an original approach to untraceable communication that avoids some of the significant shortcomings of existing methods. Using non-interactive privacy-preserving aggregation as an underlying building block we achieve attractive features, including unsurpassed low computational and transmission overhead of only 3 encryptions per participant in only a single round.

Sigurd Eskeland, Svetlana Boudko

PointPuff: An Ed25519 Optimization Implementation

Abstract

Data transmission and interaction in a network can not be separated from the digital signatures. In recent years, Ed25519 algorithm has attracted extensive attention for its “High-speed and High-security” features. However, as shown by some test data, the performance of Ed25519, especially in terms of signature verification, remains unsatisfactory. Therefore, we improved the algorithm of Ed25519 batch verification in all three layers of elliptic curve arithmetic. We put forward a new point structure called PointPuff to accelerate the point-checking and point add processes, improve the traditional elliptic curve multi-scalar multiplication operation, and design a new finite-field large integer multiplication operation. In our test, the optimized batch verification performance was 50.04% higher than the existing algorithm, which was consistent with the theoretical analysis and within the error range.

Mengqing Yang, Chunxiao Ye, Yuanmu Liu, Yan Jin, Chunming Ye

Detecting Web Tracking at the Network Layer

Abstract

Third-party tracking allows companies to identify users and track their online activity across different websites or digital services. This paper presents a first experimental study to detect advertisements and tracker by inspecting fully encrypted network transactions at the TCP/IP network level associated with a website. The first results are encouraging and motivate to extend this first proof-of-concept study even further in the future. A classical application area in the future would be the use in areas where communication can only be accessed on encrypted TCP/IP level (keyword secure IoT environments) or the presented approach is used simply to enable a classical extension of the portfolio for tracker detection.

Maximilian Wittig, Doğan Kesdoğan

What’s Inside a Node? Malicious IPFS Nodes Under the Magnifying Glass

Abstract

InterPlanetary File System (IPFS) is one of the most promising decentralized off-chain storage mechanisms, particularly relevant for blockchains, aiming to store the content forever, thus it is crucial to understand its composition, deduce actor intent and investigate its operation and impact. Beyond the network functionality that IPFS offers, assessing the quality of nodes, i.e. analysing and categorising node software and data, is essential to mitigate possible risks and exploitation of IPFS. To this end, in this work we took three daily snapshots of IPFS nodes within a month and analysed each node (by IP address) individually, using threat intelligence feeds. The above enabled us to quantify the number of potentially malicious and/or abused nodes. The outcomes lead us to consider using a filter to isolate malicious nodes from the network, an approach we implemented as a prototype and used for assessment of effectiveness.

Christos Karapapas, George C. Polyzos, Constantinos Patsakis

Quantum-Secure Communication for Trusted Edge Computing with IoT Devices

Abstract

Internet-of-Things(IoT)-based edge computing in smart factories, smart grid, agriculture, constructions and autonomous vehicles include service-oriented gateways that connect with the cloud, perform machine-to-machine communication, often transmiting large amount data up and down the network, performing time-sensitive processing and involving intelligent local decision-making. In view of a sharp increase in cyberattacks today targeting edge computing, these gateways need to provide digital signing and key negotiation for ensuring reliable data sources, trusted applications and authentic devices and connections. In contrast to common perception, we show that post-quantum cryptography methods do not necessitate extensive modification to adopt in such environments; further, the cryptography algorithm’s hardness is preserved while fulfilling the IoT device’s resource limitations. In particular, we demonstrate an efficient method and an implementation on a 32-bit ARMCortex-M4, 64KB memory microcontroller, based on post-quantum key encapsulation mechanisms (KEMs), for secure communication and authentication in an industrial IoT environment.

George Kornaros, Georgia Berki, Miltos Grammatikakis

Evaluation of a Red Team Automation Tool in Live Cyber Defence Exercises

Abstract

This paper presents an evaluation of the red team automation tool Lore in two live-fire cyber defense exercises (CDX). During the CDXs, Lore and manual “red” teams subjected 72 network security analysts (i.e., defenders; the “blue” side) to various threats such as software exploits and shell commands. Ten hypotheses related to how the actions by manual red teams and Lore are perceived and managed by the security analysts are examined. Evaluations were made by studying the subjective judgements of the analysts and by comparing the objective ground truth to their submitted incident reports. The results show that none of the null hypotheses could be rejected. In other words, the security analysts could not tell the difference between the actions made by the manual red team and those made by Lore, and their performance was similar regardless of the source of the threats.

Hannes Holm, Jenni Reuben

Automated and Improved Detection of Cyber Attacks via an Industrial IDS Probe

Abstract

Network flow classification allows to distinguish normal flows from deviant behaviors. However, given the diversity of the approaches proposed for intrusion detection via IDS probes, an adequate fundamental solution is required. Indeed, most of existing solutions address a specific context which does not allow to assess the efficiency of the proposed models on a different context. Therefore, we propose in this paper an approach for malicious flow detection based on One Dimensional Convolutional Neural Networks (1D-CNN). Our solution extracts features based on the definition of network flows. Thus, it can be common to any network flow classification model. This feature engineering phase is coupled to CNN’s feature detector in order to provide an efficient classification approach. To evaluate its performance, our solution has been evaluated on two different datasets (a recent dataset extracted from a real IBM industrial context and the NSL-KDD dataset that is widely used in the literature). Moreover, a comparison with existing solutions has been provided to NSL-KDD dataset. Attacks in both datasets have been defined using the globally-accessible knowledge base of adversary tactics and techniques MITRE framework. The evaluation results have shown that our proposed solution allows an efficient and accurate classification in both datasets (with an accuracy rate of 94% at least). Moreover, it outperforms existing solutions in terms of classification metrics and execution time as well.

Almamy Touré, Youcef Imine, Thierry Delot, Antoine Gallais, Alexis Semnont, Robin Giraudo

An Accurate and Real-Time Detection Method for Concealed Slow HTTP DoS in Backbone Network

Abstract

Slow HTTP DoS (SHD) is a type of DoS attack based on HTTP/HTTPS. SHD traffic at the application layer may be encrypted. Besides, the interval between packets can reach tens of seconds or more due to its slow sending rate. Therefore, SHD is concealed for detection. The methods for detecting high-speed DoS are not suitable for detecting the attack, making detection for SHD a challenging problem. Some existing SHD detection methods are complex and computationally intensive, making it hard to meet the demand for real-time in backbone networks. In addition, most of these methods are based on bidirectional traffic and do not consider the asymmetry of routing on the Internet. In this paper, based on the traffic characteristics of the most common types of SHD, we extract several representative features from unidirectional flows. These features can still work well under sampling and asymmetric routing scenarios. We also use Slow HTTP DoS Sketch to record the features quickly and accurately. In experiments that used public backbone datasets as background traffic, the results show that even with a large number of unidirectional flows and a sampling rate of 1/64, our method can still accurately detect SHD traffic within 2 min.

Jinfeng Chen, Hua Wu, Suyue Wang, Guang Cheng, Xiaoyan Hu

Towards an Information Privacy Competency Model for the Usage of Mobile Applications

Abstract

In a world where the industry of mobile applications (apps) is continuously expanding, the need for reinforcing users’ protection of information privacy is urgent. Focusing on this emerging need, this study aims at highlighting the main competencies that a user of mobile apps should hold in order to protect their information privacy. The contribution of the paper is threefold; First, it proposes a framework which describes the actions that users of mobile applications make before and after the installation of the application. Second, based on conceptual analysis, this study introduces a framework for the synthesis of the Information Privacy Competency Model for Users of Mobile Apps incorporating widely known personality theories namely Protection Motivation Theory and Big Personality Theory. Finally, synthesizes the results into indicative competencies that users of mobile apps should hold so as to be competent to protect their information privacy. This study offers important implications regarding privacy protection in mobile apps not only for users, but also for privacy researchers, online service providers and educators.

Aikaterini Soumelidou, Aggeliki Tsohou

SecPassInput: Towards Secure Memory and Password Handling in Web Applications

Abstract

JavaScript does not provide web applications the ability to overwrite or clear variables of primitive types, such as strings, when they are no longer required. Applications instead need to rely on the garbage collector to eventually clear sensitive data from memory. When accessing input fields natively provided by the browser via JavaScript, their values are accessed through primitive type variables and thus affected by this limitation.

In this paper, we analyze how the popular browsers Chrome, Chromium, Firefox, Opera, and Edge handle input values in memory. We find that sensitive values almost always remain in memory several minutes longer than necessary.

We propose the JavaScript library SecPassInput that simulates a non-native input for passwords. The library does not rely on variables of a primitive type, thereby giving web applications the ability to clear and overwrite values in memory. We evaluate the security benefits of SecPassInput by measuring how long values remain in memory after they are no longer needed, finding that the on-screen keyboard of SecPassInput guarantees immediate removal from memory after triggering SecPassInput ’s clear operation.

Pascal Wichmann, August See, Hannes Federrath

Bl0ck: Paralyzing 802.11 Connections Through Block Ack Frames

Abstract

Despite Wi-Fi is at the eve of its seventh generation, security concerns regarding this omnipresent technology remain in the spotlight of the research community. This work introduces two new denial of service (DoS) attacks against contemporary Wi-Fi 5 and 6 networks. Differently from similar works in the literature which focus on 802.11 management frames, the introduced assaults exploit control frames. Both these attacks target the central element of any infrastructure-based 802.11 network, i.e., the access point (AP), and result in depriving the associated stations of any service. We demonstrate that, at the very least, the attacks affect a great mass of off-the-self AP implementations by different renowned vendors, and they can be mounted with inexpensive equipment, little effort, and a low level of expertise. With reference to the latest standard, namely, 802.11-2020, we elaborate on the root cause of the respected vulnerabilities, pinpointing shortcomings. Following a coordinated vulnerability disclosure process, our findings have been promptly communicated to each affected AP vendor, already receiving positive feedback, as well as, at the time of writing, a reserved common vulnerabilities and exposures (CVE) identifier, namely CVE-2022-32666.

Efstratios Chatzoglou, Vyron Kampourakis, Georgios Kambourakis

Enhancing the ACME Protocol to Automate the Management of All X.509 Web Certificates

Abstract

X.509 Public Key Infrastructures (PKIs) are widely used for managing X.509 Public Key Certificates (PKCs) to allow for secure communications and authentication on the Internet. PKCs are issued by a trusted third-party Certification Authority (CA), which is responsible for verifying the certificate requester’s information. Recent developments in web PKI show a high proliferation of Domain Validated (DV) certificates but a decline in Extended Validated (EV) certificates, indicating poor authentication of the entities behind web services. The ACME protocol facilitates the deployment of Web Certificates by automating their management. However, it is only limited to DV certificates. This paper proposes an enhancement to the ACME protocol for automating all types of Web X.509 PKCs by using W3C Verifiable Credentials (VCs) to assert a requester’s claims. We argue that any CA’s requirements for issuing a PKC can be expressed as a set of VCs, returned in a Verifiable Presentation (VP). We propose a generic communication workflow to request and present VPs, and provide proof-of-concept of the viability of our approach.

David A. Cordova Morales, Ahmad Samer Wazan, David W. Chadwick, Romain Laborde, April Rains Reyes Maramara, Kalil Cabral

MADONNA: Browser-Based MAlicious Domain Detection Through Optimized Neural Network with Feature Analysis

Abstract

The detection of malicious domains often relies on machine learning (ML), and proposals for browser-based detection of malicious domains with high throughput have been put forward in recent years. However, existing methods suffer from limited accuracy. In this paper, we present MADONNA, a novel browser-based detector for malicious domains that surpasses the current state-of-the-art in both accuracy and throughput. Our technical contributions include optimized feature selection through correlation analysis, and the incorporation of various model optimization techniques like pruning and quantization, to enhance MADONNA’s throughput while maintaining accuracy. We conducted extensive experiments and found that our optimized architecture, the Shallow Neural Network (SNN), achieved higher accuracy than standard architectures. Furthermore, we developed and evaluated MADONNA’s Google Chrome extension, which outperformed existing methods in terms of accuracy and F1-score by six points (achieving 0.94) and four points (achieving 0.92), respectively, while maintaining a higher throughput improvement of 0.87 s. Our evaluation demonstrates that MADONNA is capable of precisely detecting malicious domains, even in real-world deployments.

Janaka Senanayake, Sampath Rajapaksha, Naoto Yanai, Chika Komiya, Harsha Kalutarage

Cyber Key Terrain Identification Using Adjusted PageRank Centrality

Abstract

The cyber terrain contains devices, network services, cyber personas, and other network entities involved in network operations. Designing a method that automatically identifies key network entities to network operations is challenging. However, such a method is essential for determining which cyber assets should the cyber defense focus on. In this paper, we propose an approach for the classification of IP addresses belonging to cyber key terrain according to their network position using the PageRank centrality computation adjusted by machine learning. We used hill climbing and random walk algorithms to distinguish PageRank’s damping factors based on source and destination ports captured in IP flows. The one-time learning phase on a static data sample allows near-real-time stream-based classification of key hosts from IP flow data in operational conditions without maintaining a complete network graph. We evaluated the approach on a dataset from a cyber defense exercise and on data from the campus network. The results show that cyber key terrain identification using the adjusted computation of centrality is more precise than its original version.

Lukáš Sadlek, Pavel Čeleda

Machine Learning Metrics for Network Datasets Evaluation

Abstract

High-quality datasets are an essential requirement for leveraging machine learning (ML) in data processing and recently in network security as well. However, the quality of datasets is overlooked or underestimated very often. Having reliable metrics to measure and describe the input dataset enables the feasibility assessment of a dataset. Imperfect datasets may require optimization or updating, e.g., by including more data and merging class labels. Applying ML algorithms will not bring practical value if a dataset does not contain enough information. This work addresses the neglected topics of dataset evaluation and missing metrics. We propose three novel metrics to estimate the quality of an input dataset and help with its improvement or building a new dataset. This paper describes experiments performed on public datasets to show the benefits of the proposed metrics and theoretical definitions for more straightforward interpretation. Additionally, we have implemented and published Python code so that the metrics can be adopted by the worldwide scientific community.

Dominik Soukup, Daniel Uhříček, Daniel Vašata, Tomáš Čejka

Factors of Intention to Use a Photo Tool: Comparison Between Privacy-Enhancing and Non-privacy-enhancing Tools

Abstract

Tools that detect and transform privacy sensitive information in user content have been proposed to enhance privacy in contexts such as social media. However, previous research has found that privacy-related concerns can be higher in these types of tools compared to similar non-privacy tools. In this paper, we focus on adoption of these tools and investigate how the knowledge that a data-processing tool has a privacy purpose affects privacy-related factors of intention to use such a tool, when compared with a similar tool with a non-privacy-related purpose. We conducted a user study where we described a privacy-enhancing and a non-privacy-enhancing photo manipulation app to two groups of participants. The results show that general and context-specific privacy-related perception has different effects for the two types of apps. In particular, although participants perceived the same level of privacy risk towards both types of apps, this risk only had a significant negative effect on intention to use in the case of the privacy-enhancing app. Furthermore, disposition to value privacy increased both perceived risk and intention to use the privacy-enhancing app. We discuss these findings in the context of the diffusion of privacy-enhancing tools for user content.

Vanessa Bracamonte, Sebastian Pape, Sascha Löbner

Real-Time Platform Identification of VPN Video Streaming Based on Side-Channel Attack

Abstract

The video platforms that users watch leak the privacy of their preferences. More and more video streaming is being encrypted to protect users’ privacy. In addition, many users use VPN to enhance their privacy protection further. VPN makes video platform identification challenging because it poses traffic obfuscation and further data encryption. Although the segment-based transmission mechanism and Variable Bit-Rate encoding in HAS make network video traffic show still identifiable patterns, most existing work cannot distinguish different platforms due to the similarity of video streaming. Therefore, we propose a traffic-based side-channel attack method to identify VPN video streaming platforms in real time. The aggregated feature sequence of the unidirectional video streaming is extracted to significantly retain the characteristics of different video platforms. Experiments on 10Gbps backbone background traffic show that the F1-score of the method exceeds 97% and can be processed in real time. In addition, we verify the method’s robustness on datasets with different path features and encryption techniques. A comparison with similar methods shows that our method only requires 1/1260 of the storage and 1/60 of the processing time to identify accurately.

Anting Lu, Hua Wu, Hao Luo, Guang Cheng, Xiaoyan Hu

Toward the Establishment of Evaluating URL Embedding Methods Using Intrinsic Evaluator via Malicious URLs Detection

Abstract

In order to compare the performance of the malicious URLs detection method, researches used the F-score or other detection accuracy to evaluate, but there are some difficulties in evaluating the URL embedding method used in malicious URLs detection because the detection accuracy is also effect by machine learning or deep learning models and data sets. An evaluation method of URL embedding method that is not affected by other factors is particularly important. In this paper, we proposed an intrinsic evaluation method for URL embedding method that is not affected by machine learning models or deep learning models and data sets. Besides, We analyse some URL embedding methods according to intrinsic and extrinsic methods and offer a guidance in selecting suitable embedding methods in URL by analysing the results.

Qisheng Chen, Kazumasa Omote

Key Management Based on Ownership of Multiple Authenticators in Public Key Authentication

Abstract

Public key authentication (PKA) has been deployed in various services to provide stronger authentication to users. In PKA, a user manages private keys on her devices called authenticators, and registers public keys to services. To protect private keys, authenticators are usually designed not to export private keys outside. Nowadays, a user has multiple authenticators like PCs and smartphones, and struggles to manage multiple public keys in many services every time she starts to use new services and replaces some of her authenticators. To ease the burden, we propose a mechanism where users and services manage public keys based on the owner of authenticators and users can access services with PKA using any of their authenticators. We introduce a key pair called an Ownership Verification Key (OVK) consisting of the private key (OVSK) and the public key (OVPK). All authenticators owned by a user derive the same OVSK from the pre-shared secret. Services verify the ownership of the authenticators using the OVPK to decide whether binding the requested public key to her account. To protect user privacy, authenticators generate an unique OVK for each service. We implement the Proof-of-Concept, show that our proposal achieves some security goals, and discuss how to mitigate threats not completely handled.

Kodai Hatakeyama, Daisuke Kotani, Yasuo Okabe

Backmatter