004 Datenverarbeitung; Informatik
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Institute
Blockchains and distributed ledger technology (DLT) that rely on Proof-of-Work (PoW) typically show limited performance. Several recent approaches incorporate Byzantine fault-tolerant (BFT) consensus protocols in their DLT design as Byzantine consensus allows for increased performance and energy efficiency, as well as it offers proven liveness and safety properties. While there has been a broad variety of research on BFT consensus protocols over the last decades, those protocols were originally not intended to scale for a large number of nodes. Thus, the quest for scalable BFT consensus was initiated with the emerging research interest in DLT. In this paper, we first provide a broad analysis of various optimization techniques and approaches used in recent protocols to scale Byzantine consensus for large environments such as BFT blockchain infrastructures. We then present an overview of both efforts and assumptions made by existing protocols and compare their solutions.
In the last decade, crowdsourcing has proved its ability to address large scale data collection tasks, such as labeling large data sets, at a low cost and in a short time. However, the performance and behavior variability between workers as well as the variability in task designs and contents, induce an unevenness in the quality of the produced contributions and, thus, in the final output quality. In order to maintain the effectiveness of crowdsourcing, it is crucial to control the quality of the contributions. Furthermore, maintaining the efficiency of crowdsourcing requires the time and cost overhead related to the quality control to be at its lowest. While effective, current quality control techniques such as contribution aggregation, worker selection, context-specific reputation systems, and multi-step workflows, suffer from fairly high time and budget overheads and from their dependency on prior knowledge about individual workers.
In this thesis, we address this challenge by leveraging the similarity between completed and incoming tasks as well as the correlation between the worker declarative profiles and their performance in previous tasks in order to perform an efficient task-aware worker selection. To this end, we propose CAWS (Context AwareWorker Selection) method which operates in two phases; in an offline phase, completed tasks are clustered into homogeneous groups for each of which the correlation with the workers declarative profile is learned. Then, in the online phase, incoming tasks are matched to one of the existing clusters and the correspondent, previously inferred profile model is used to select the most reliable online workers for the given task. Using declarative profiles helps eliminate any probing process, which reduces the time and the budget while maintaining the crowdsourcing quality. Furthermore, the set of completed tasks, when compared to a probing task split, provides a larger corpus from which a more precise profile model can be learned. This translates to a better selection quality, especially for harder tasks.
In order to evaluate CAWS, we introduce CrowdED (Crowdsourcing Evaluation Dataset), a rich dataset to evaluate quality control methods and quality-driven task vectorization and clustering. The generation of CrowdED relies on a constrained sampling approach that allows to produce a task corpus which respects both, the budget and type constraints. Beside helping in evaluating CAWS, and through its generality and richness, CrowdED helps in plugging the benchmarking gap present in the crowdsourcing quality control community.
Using CrowdED, we evaluate the performance of CAWS in terms of the quality of the worker selection and in terms of the achieved time and budget reduction. Results shows the following: first, automatic grouping is able to achieve a learning quality similar to job-based grouping. And second, CAWS is able to outperform the state-of-the-art profile-based worker selection when it comes to quality. This is especially true when strong budget and time constraints are present on the requester side.
Finally, we complement our work by a software contribution consisting of an open source framework called CREX (CReate Enrich eXtend). CREX allows the creation, the extension and the enrichment of crowdsourcing datasets. It provides the tools to vectorize, cluster and sample a task corpus to produce constrained task sets and to automatically generate custom crowdsourcing campaign sites.
The Semantic Web exists for about 20 years by now, but its applicability as well as its presence does not live up to the standards of its original idea. Incorporated Semantic Web Technologies do have an initial barrier to learn and apply, which can discourage many potential users. This leads to less available data overall in addition to decreased data quality.
This work solves parts of the aforementioned problem by supporting idiomatic entry to those Semantic Web Technologies, allowing for "easier" accessibility and usability. Anno4j is a Java library that implements a form of Object-Relational Mapping for RDF data. With its application, RDF data can be created via a mapping by simply instantiating Java objects - an object-oriented programming concept the user is familiar with. On the other side, requesting persisted data is supported by a path-based querying possibility, while other features like transactional behaviour, code generation, and automated validation of input contribute to a more effective, comprehensive, and straightforward usage.
A use-case is provided by the MICO Platform, a centralized software instance that connects autonomous multimedia extractors in a workflow-driven fashion. This leads to a rich metadata background for the inserted multimedia files, enabling them to be used in diverse scenarios as well as unlocking yet hidden semantics. For this task it was necessary to design and implement a metadata model that is able to aggregate and merge the varying extractor results under a common denominator: the MICO Metadata Model.
The results of this work allow the use case to incorporate idiomatic Semantic Web Technologies which are then usable natively by non-Semantic Web experts. Additionally, an increase has been achieved in forms of data integration, synchronisation, integrity and validity, as well as an overall more comprehensive and rich implementation of the multimedia extractors.
Direct access to the system's resources such as the GPU, persistent storage and networking has enabled in-browser crypto-mining. Thus, there has been a massive response by rogue actors who abuse browsers for mining without the user's consent. This trend has grown steadily for the last months until this practice, i.e., CryptoJacking, has been acknowledged as the number one security threat by several antivirus companies.
Considering this, and the fact that these attacks do not behave as JavaScript malware or other Web attacks, we propose and evaluate several approaches to detect in-browser mining. To this end, we collect information from the top 330.500 Alexa sites. Mainly, we used real-life browsers to visit sites while monitoring resource-related API calls and the browser's resource consumption, e.g., CPU.
Our detection mechanisms are based on dynamic monitoring, so they are resistant to JavaScript obfuscation. Furthermore, our detection techniques can generalize well and classify previously unseen samples with up to 99.99\% precision and recall for the benign class and up to 96\% precision and recall for the mining class. These results demonstrate the applicability of detection mechanisms as a server-side approach, e.g., to support the enhancement of existing blacklists.
Last but not least, we evaluated the feasibility of deploying prototypical implementations of some detection mechanisms directly on the browser. Specifically, we measured the impact of in-browser API monitoring on page-loading time and performed micro-benchmarks for the execution of some classifiers directly within the browser. In this regard, we ascertain that, even though there are engineering challenges to overcome, it is feasible and beneficial for users to bring the mining detection to the browser.
This thesis distills technical requirements for an increased probative value and data protection compliance, and maps them onto cryptographic properties for which it constructs provably secure and especially private malleable signature schemes (MSS). MSS are specialised digital signature schemes that allow the signatory to authorize certain subsequent modifications, which will not negatively affect the signature verification result.
Legally, regulations such as European Regulation 910/2014 (eIDAS), ‘follow-up’ to longstanding Directive 1999/93/EC, describe the requirements in technology-neutral language. eIDAS states that, when a digital signature meets the full requirements it becomes a qualified electronic signature and then it “[...] shall have the equivalent legal effect of a handwritten signature [...]” [Art. 25 Regulation 910/2014]. The question of what legal effect this has with regards to the probative value that is assigned is actually not determined in EU Regulation 910/2014 but in European member state law. This thesis concentrates in its analysis on the — in this respect detailed — German Code of Civil Procedure (ZPO). Following the ZPO, a signature awards the signed document with at least a high probative value of prima facie evidence. For signed documents of official authority the ZPO’s statutory rules even award evidence with a legal presumption of authenticity. This increased probative value is also awarded to electronic documents bearing electronic signatures when those conform to the eIDAS requirements. The requirements centre around the technical security goals of integrity and accountability. Technical mechanisms use cryptographic means to detect the absence of unauthorized modifications (integrity) and allow to authenticate the signed document’s signatory (accountability).
However, the specialised malleable signature schemes’ main advantage is a cryptographic property termed privacy: An authorized subsequent modification will protect the confidentiality of the modified original. Moreover, the MSS will retain a verifiable signature if only authorized modifications were carried out. If these properties are reached with provable security the schemes are called private malleable signature schemes. This thesis analyses two forms of MSS discussed in existing literature: Redactable signature schemes (RSS) which allow subsequent deletions, and sanitizable signature schemes (SSS) which allow subsequent edits. These two forms have many application scenarios: A signatory can delegate that a later redaction might take place while retaining the integrity and authenticity protection for the still remaining parts. The verification of a signature on a redacted or sanitized document still enables the verifying entity to corroborate the signatory’s identity with the help of flanking technical and organisational mechanisms, e.g. a trusted public key infrastructure. The valid signature further corroborates the absence of unauthorized changes, because the MSS is still cryptographically protecting the signed document from undetected unauthorized changes inflicted by adversaries. Due to the confidentiality protection for the overwritten parts of the document following from cryptographic privacy the sanitization and redaction can be used to safeguard personal data to comply with data protection regulation or withhold trade-secrets.
The research question is: Can a malleable signature scheme be private to be compliant with EU data protection regulation and at the same time fulfil the integrity protection legally required in the EU to achieve a high probative value for the data signed?
Answering this requires to understand the protection requirements in respect to accountability and integrity rooted in Regulation 910/2014 and related legal texts. This thesis has analysed the previous Directive 1999/93/EC as well as German SigG and SigVO or UK and US laws. Besides that, legal texts, laws and regulations for the protection requirements of personal data (or PII) have been analysed to distill the confidentiality requirements, e.g. the German BDSG or the EU Regulation 2016/679 (GDPR). Moreover, an answer to the research question entails understanding the relevant difference between regular digital signature schemes, like RSASSA-PSS from PKCS-v2.2 [422], which are legally accepted mechanisms for generating qualified electronic signatures and MSS for which the legal status was completely unknown before the thesis. Especially as MSS allow the authorized entity to adapt the signature, such that it is valid after the authorized modification, without the knowledge or use of the signatory’s signature generation key. On verification of an MSS the verifying entity still sees a valid signature technically appointing the legal signatory as the origin of a document, which might — however — have undergone authorized modifications after the signature was applied.
The thesis documents the results achieved in several domains:
1. Analysis of legal requirements towards integrity protection for an increased probative value and towards the confidentiality protection for use as a privacy-enhancing-technique to comply with data protection regulation.
2. Definition of a suitable terminology for integrity protection to capture (a) the differences between classical and malleable signature schemes, (b) the subtleties among existing MSS, as well as (c) the legal requirements.
3. Harmonisation of existing MSS and their cryptographic properties and the analysis of their shortcomings with respect to the legal requirements.
4. Design of new cryptographic properties and their provably secure cryptographic instantiations, i.e., the thesis proposes nine new cryptographic constructions accompanied by rigorous proofs of their security with respect to the formally defined cryptographic properties.
5. Final evaluation of the increased probative value and data-protection level achievable through the eight proposed cryptographic malleable signature schemes.
The thesis concludes that the detection of any subsequent modification (authorized and unauthorized) is of paramount legal importance in order to meet EU Regulation 910/2014. Further, this thesis formally defined a public form of the legally requested integrity verification which allows the verifying entity to corroborate the absence of any unauthorized modifications with a valid signature verification while simultaneously detecting the presence of an authorized modification — if at least one such authorized modification has subsequently occurred. This property, called non-interactive public accountability (PUB), has been formally defined in this thesis, was published and has already been adopted by the academic community. It was carefully conceived to not negatively impact a base-line level of privacy protection, as non-interactive public accountability had to destroy an existing strong privacy notion of transparency, which was identified as a hinderance to legal equivalence arguments. With RSS and SSS constructions that meet these properties, the thesis can give a positive answer to the research question:
Private MSS can reach a level of integrity protection and guarantee a level of accountability comparable to that of technical mechanisms that are legally accepted to generate qualified electronic signatures giving an increased probative value to the signed document, while at the same time protect the overwritten contents’ confidentiality.
The Linear Ordering problem consists in finding a total ordering of the vertices of a directed graph such that the number of backward arcs, i.e., arcs whose heads precede their tails in the ordering, is minimized. A minimum set of backward arcs corresponds to an optimal solution to the equivalent Feedback Arc Set problem and forms a minimum Cycle Cover.
Linear Ordering and Feedback Arc Set are classic NP-hard optimization problems and have a wide range of applications. Whereas both problems have been studied intensively on dense graphs and tournaments, not much is known about their structure and properties on sparser graphs. There are also only few approximative algorithms that give performance guarantees especially for graphs with bounded vertex degree.
This thesis fills this gap in multiple respects: We establish necessary conditions for a linear ordering (and thereby also for a feedback arc set) to be optimal, which provide new and fine-grained insights into the combinatorial structure of the problem. From these, we derive a framework for polynomial-time algorithms that construct linear orderings which adhere to one or more of these conditions. The analysis of the linear orderings produced by these algorithms is especially tailored to graphs with bounded vertex degrees of three and four and improves on previously known upper bounds. Furthermore, the set of necessary conditions is used to implement exact and fast algorithms for the Linear Ordering problem on sparse graphs. In an experimental evaluation, we finally show that the property-enforcing algorithms produce linear orderings that are very close to the optimum and that the exact representative delivers solutions in a timely manner also in practice.
As an additional benefit, our results can be applied to the Acyclic Subgraph problem, which is the complementary problem to Feedback Arc Set, and provide insights into the dual problem of Feedback Arc Set, the Arc-Disjoint Cycles problem.