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Institute
Internet voting systems are supposed to meet the same high stan-
dards as traditional paper-based systems when used in real political
elections: freedom of choice, universal and equal suffrage, secrecy
of the ballot, and independent verifiability of the election result.
Although numerous Internet voting systems have been proposed
to achieve these challenging goals simultaneously, few come close
in reality.
We propose a novel publicly verifiable and practically efficient
Internet voting system, DeVoS, that advances the state of the art.
The main feature of DeVoS is its ability to protect voters’ freedom
of choice in several dimensions. First, voters in DeVoS can intu-
itively update their votes in a way that is deniable to observers but
verifiable by the voters; in this way voters can secretly overwrite
potentially coerced votes. Second, in addition to (basic) vote privacy,
DeVoS also guarantees strong participation privacy by end-to-end
hiding which voters have submitted ballots and which have not.
Finally, DeVoS is fully compatible with Perfectly Private Audit Trail,
a state-of-the-art Internet voting protocol with practical everlasting
privacy. In combination, DeVoS offers a new way to secure free
Internet elections with strong and long-term privacy properties.
Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike.
Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
(2023)
Tor is the most popular anonymization network with millions of daily users. This makes it an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods preferring Tor nodes with higher resilience to active BGP attacks.
In this work, we analyze the implications of such proposals and demonstrate that two state-of-the-art path selection methods, namely Counter-RAPTOR and DPSelect, are not as secure as thought before. First, we show that DPSelect achieves only one third of its originally claimed resilience and, thus, is not as resilient as widely accepted. Second, we reveal that the resilience to active BGP attacks on the way back, i.e., from the first anonymization node to a given Tor user, provided by both methods is significantly lower than on the forward path. Beside their lower resilience in specific cases, we show that for particular users the usage of Counter-RAPTOR and DPSelect also leads to leakage of user’s location. Furthermore, we uncover the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious Tor middle node can fingerprint the user’s location and the confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.
The overwhelmingly widespread use of Internet of Things (IoT) in different application domains brought not only benefits, but, alas, security concerns as a result of the increased attack surface and vectors. One of the most critical mechanisms in IoT infrastructure is key management. This paper reflects on the problems and challenges of existing key management systems, starting with the discussion of a recent real-world attack. We identify and elaborate on the drawbacks of security primitives based purely on physical variations and – after highlighting the problems of such systems – continue on to deduce an effective and cost-efficient key management solution for IoT systems extending the symbiotic security approach in a previous work. The symbiotic architecture combines software, firmware, and hardware resources for secure IoT while avoiding the traditional scheme of static key storage and generating entropy for key material on-the-fly via a combination of a Physical Unclonable Func tion (PUF) and pseudo-random bits pre-populated in firmware.
Vote privacy is a fundamental right, which needs to be protected not only during an election, or for a limited time afterwards, but for the foreseeable future. Numerous electronic voting (e-voting) protocols have been proposed to address this challenge, striving for everlasting privacy. This property guarantees that even computationally unbounded adversaries cannot break privacy of past elections. The broad interest in secure e-voting with everlasting privacy has spawned a large variety of protocols over the last three decades. These protocols differ in many aspects, in particular the precise security properties they aim for, the threat scenarios they consider, and the privacy-preserving techniques they employ. Unfortunately, these differences are often opaque, making analysis and comparison cumbersome. In order to overcome this non-transparent state of affairs, we systematically analyze all e-voting protocols designed to provide everlasting privacy. First, we illustrate the relations and dependencies between all these different protocols. Next, we analyze in depth which protocols do provide secure and efficient approaches to e-voting with everlasting privacy under realistic assumptions, and which ones do not. Eventually, based on our extensive and detailed treatment, we identify which research problems in this field have already been solved, and which ones are still open. Altogether, our work offers a well - founded reference point for conducting research on secure e - voting with everlasting privacy as well as for future - proofing privacy in real - world electronic elections.
Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls
(2023)
During the first days of the 2022 Russian invasion of Ukraine, Russia’s media regulator blocked access to many global social media
platforms and news sites, including Twitter, Facebook, and the
BBC. To bypass the information controls set by Russian authorities,
pro-Ukrainian groups explored unconventional ways to reach out
to the Russian population, such as posting war-related content in
the user reviews of Russian business available on Google Maps or
Tripadvisor. This paper provides a first analysis of this new phenomenon by analyzing the creative strategies to avoid state censorship.
Specifically, we analyze reviews posted on these platforms from
the beginning of the conflict to September 2022. We measure the
channeling of war messages through user reviews in Tripadvisor
and Google Maps, as well as in VK, a popular Russian social network.
Our analysis of the content posted on these services reveals
that users leveraged these platforms to seek and exchange humanitarian
and travel advice, but also to disseminate disinformation and
polarized messages. Finally, we analyze the response of platforms
in terms of content moderation and their impact.
Controller Area Network (CAN) is a masterless serial bus designed and widely used for the exchange of mission and time-critical information within commercial vehicles. In-vehicle communication is based on messages sent and received by Electronic Control Units (ECUs) connected to this serial bus network. Although unencrypted, CAN messages are not easy to interpret. In fact, Original Equipment Manufacturers (OEMs) attempt to achieve security through obscurity by encoding the data in their proprietary format, which is kept secret from the general public. As a result, the only way to obtain clear data is to reverse engineer CAN messages. Driven by the need for in-vehicle message interpretation, which is highly valuable in the automotive industry, researchers and companies have been working to make this process automated, fast, and standardized. In this paper, we provide a comprehensive review of the state of the art and summarize the major advances in CAN bus reverse engineering. We are the first to provide a taxonomy of CAN tokenization and translation techniques. Based on the reviewed literature, we highlight an important issue: the lack of a public and standardized dataset for the quantitative evaluation of translation algorithms. In response, we define a complete set of requirements for standardizing the data collection process. We also investigate the risks associated with the automation of CAN reverse engineering, in particular with respect to the security network and the safety and privacy of drivers and passengers. Finally, we discuss future research directions in CAN reverse engineering.
This paper presents a tool to Explore Process Discovery (EPD) results using activity projection. Our EPD-Tool aims at exploring quality changes after removing activities from an event log. The main idea is to create a projected event log for every non-empty subset of activities and apply process discovery and conformance checking on them. The tool has been implemented as a plugin in ProM. First, EPD-Tool uses a process discovery algorithm to discover Petri net models for each projected event log. Then, EPD-Tool uses a conformance checking technique to compute conformance measures for each projected event log and model pair (L, N), e.g., fitness, precision, and F1-score. Finally, a dendrogram is generated to visualize the relationship between each log-model pair, thus enabling the systematic exploration of the different models using the dendrogram to find the best-performing node, i.e., a best log-model pair. This method prioritizes activities and detects redundancy in the process, which contributes to process enhancement. Conversely, critical activities are uncovered to help to shorten the processing time or save the process cost. This paper presents the EPD-Tool implementation and some example results.