This paper contributes to research on gamified learning by providing lessons learned from a gamified, self-paced, digital distant learning environment on business information systems. It expands previous research by reporting on a second iteration of an educational design experiment in S/4 HANA. Design elements were composed into a playful layout and associations with a non-learning-context were included, in order to create a more relaxed learning experience. Mistakes were allowed, to keep the students’ attention high and to make achievements more rewarding. A mixed media approach was applied to enrich students’ learning experience and to avoid monotony. This paper contributes to the body of knowledge by providing lessons learned from a radical conversion of a traditional face-to-face course into a digital distant learning environment using elements of gamification, self-paced learning, and learning from mistakes.
Hashing to prime in zero-knowledge takes as input a committed secret $$a$$ -bit input x and outputs a committed secret $${(k+1)}$$ -bit prime number $$p_x$$ . In this work we are considering zero-knowledge arguments that establish that the corresponding computations are done correctly and that the resulting commitment is indeed on the prime number derived from x. Depending on the number of zero-knowledge argument rounds k and the number of primality bases t used to establish primality, we obtain a soundness error probability of at most $$2^{-k}+ 2^{-t}$$ . While the corresponding ZK arguments have already been established in earlier work [22], we explore the use of hash-to-prime ZK arguments to enable dynamic encodings for a selection of cryptographic schemes, namely prime-based accumulator [2, 10], attribute-based credential [7], and graph signature schemes [19, 21].
Teaching and research have been the core activities of universities since ages. Nowadays, the topic of transfer is increasingly coming into focus, as universities are no longer seen as “ivory towers” in which research is cut off from the rest of the society, but rather as establishments with a profound knowledge and technology transfer. However, transfer often still occurs in an uncoordinated manner without distinct processes or coordination. A digital transformation of transfer activities could support and make universities fit for the future of transfer. Therefore, this paper does not only propose a framework for digital transformation of transfer, but also points out the importance of platforms and collaboration of universities.
Protecting software from illegal reverse engineering and malicious hackers is often remedied through either legal or technical means. In the technical domain, software obfuscation provides less than perfect protection against such attacks since there is no perfect obfuscator for all classes of programs. However, semantic preserving transformations can attempt to make the cost of attacks prohibitive in either time or resources. Software-based hardware abstraction (SBHA) is a novel approach that transforms traditional software code segments into a digital logic form and thus virtualizes code into a hardware abstraction. SBHA can be used to protect embedded secrets in programs that are used to guard intellectual property (IP). Secrets such as passwords, PINs, and activation codes authorize legitimate end-users to install or activate software for use and are validated typically through point functions that check for the single unique input that is expected. In this study we extend initial analysis of SBHA against state-of-the-art dynamic symbolic execution (DSE) attacks in recovering embedded program secrets and consider the limits of an attacker that recovers the logic circuit netlist from an SBHA-protected program. We pose four approaches for hardened SBHA configurations and evaluate their effectiveness using typical analysis tools that cover synthesis, binary decision diagram recovery, and symbolic analysis. We show that such attacks can be mitigated by these countermeasures outright and discuss the trade-off in size and overhead relative to the relatively low-cost of SBHA point-functions stand alone. We conclude that for single use operations such as point function checks, the overhead is large but the execution runtime delta is negligible.
Given how common IoT devices that use constrained resources are becoming today, the need of the hour is communication protocols which can operate securely under such limitations. For a few years, the Internet Engineering Task Force (IETF) has been working to standardize EDHOC, an authenticated key establishment protocol for such constrained IoT devices. The first version of EDHOC was proposed in 2016. In 2018, Bruni et al. [3] used the ProVerif tool [2] to formally analyze an early version of EDHOC, which had only two key establishment methods. By 2021, the protocol had been fleshed out much more, with multiple new key establishment methods, and this version was formally analyzed using the Tamarin prover [15] in [17]. In this paper, we build on that work, by modifying the model, analyzing some new properties, and discussing some aspects of the latest EDHOC specification. In particular, we extend the modeling in [17] with trusted execution environments (TEEs), modify the way we model XOR encryption, and in addition to the properties verified in [17], we verify weak post-compromise security (PCS) as well as the secrecy and integrity of some additional data used as part of the protocol.
The rapid embracing of technology in the delivery of commerce services by various service providers often results in the user surrendering their Personally Identifiable Information (PII) to the service providers thereby subjecting user data to possible online leakage and consequently putting the safety of the user at risk. This paper is proposing enhancing the protection of user PII using a traceable One Time Password (OTP) derived from the RFC 6238 Time-based One Time Password (TOTP) standard. The approach was complemented with the use of a one-way hardware based data protector that was deliberately designed to only allow data to flow in one direction to prevent online hackers having access to user data stored by service providers. Tests were conducted on the developed solution determine its effectiveness. Accessories and tools such as Arduino microcontrollers, python programming language, Arduino IDE (programming platform) and the Proteus Simulation software. Results obtained from the experiments conducted demonstrate that the user data was being protected successfully as access from online was restricted as desired.
Implementations of block ciphers typically use Boolean shares of intermediate states to prevent side-channel analysis. Implementations of public-key cryptographic algorithms are typically protected by using the algebraic properties of the algorithms. In this work, we present an exponent splitting technique with minimal impact on performance based on Boolean shares. This method can be combined with other countermeasures to make a side-channel resistant implementation with minimal increase in execution time.
Dataset for the paper "A Classification of Software-Architectural Uncertainty regarding Confidentiality". For more information, please see the README.md.
Energy consumption data is collected the service providers and shared with various stakeholders involved in a smart grid ecosystem. The fine-grained energy consumption data is immensely useful for maintaining and operating grid services. Further, these data can be used for future consumption prediction using machine learning and statistical models and market segmentation purposes. However, sharing and releasing fine-grained energy data or releasing predictive models trained on user-specific data induce explicit violations of private information of consumers [34, 41]. Thus, the service providers may share and release aggregated statistics to protect the privacy of users aiming at mitigating the privacy risks of individual users’ consumption traces. In this chapter, we show that an attacker can recover individual users’ traces of energy consumption data by exploiting regularity and uniqueness properties of individual consumption load patterns. We propose an unsupervised attack framework to recover hourly energy consumption time-series of users without any background information. We construct the problem of assigning aggregated energy consumption meter readings to individual users as a mathematical assignment problem and solve it by the Hungarian algorithm [30, 50]. We used two real-world datasets to demonstrate an attacker’s performance in recovering private traits of users. Our results show that an attacker is capable of recovering 70
For research in medicine, economics and social sciences specific data of individuals is needed. Thus it should be publicly available, but this should not offend the privacy of each individual. Microaggregation applied to databases is a standard technique to protect privacy. It clusters similar people in larger groups to achieve so called k-anonymity – every individual is hidden in a cluster of size at least k. Then the data can be made public for all kinds of analysis, whereas other concepts like differential privacy keep the database secret and allow only specific questions about the data to be asked by outsiders. The modification of a database to achieve anonymity should be as small as possible to keep its utility – that means the loss of information should be minimized. In this respect microaggregation typically performs much better than other anonymization techniques like generalization or suppression. However, minimizing the information loss by k-anonymous microaggregation is an NP-hard optimization problem for k ≥ 3 . Not only computing optimal solutions efficiently is unlikely, nontrivial approximations are lacking, too. Therefore, a bunch of heuristics all with at least quadratic time complexity have been developed. This paper improves microaggregation significantly and provides a tradeoff between computational effort and utility. First, we make a detailed analysis and tuning of the maximum distance methodology – the common approach to generate a clustering that provides k-anonymity. We review the methods proposed so far and design a new algorithm ^*_γ that gives better utility on standard benchmarks. A different approach of quadratic time complexity based on Lloyd’s algorithm has been proposed and named ONA, but not completely analysed. This paper fills this gap and improves several steps resulting in a new algorithm ^* with better utility. Mondrian is a another approach for clustering data that can be adopted for microaggregation. It is quite fast, but typically achieves very pure utility. We improve on this and design an almost linear time algorithm that gives acceptable utility, however worse than the quadratic time algorithms. Finally, we combine both techniques, ONA and Mondrian, to construct a new class of parameterized algorithms called . They are quite fast with time complexity between almost linear and quadratic, and deliver competitive utility compared to the MDAV approach.
This study analyses the relationship between ethnic minority groups’ (EMGs) short video content and online viewers’ watching interest based on the short video platform background. It considers China’s short video interactive environment and divides the cultural content into the cultural vision unit and cultural audition unit according to the sensory marketing theory. Through testing numerous EMG video content based on the covariance analysis method, the paper presents the relationships between short cultural video content and online viewers’ watching interest. Meanwhile, because of the differences among Chinese EMGs, this paper divides them into seven main groups and promotes the multi-group analysis, aiming to present specific results for each major group. The research findings can guide EMG video producers to improve their video content and contribute to Chinese EMG cultural protection and cultural diversity.
Scientific digital libraries are characterized by high requirements for the quality of digital copies of printed scientific sources, since any ambiguity or contamination within chemical formulas or mathematical expressions can lead to erroneous perception or misunderstanding of the meaning. Special requirements for digital copies are also imposed when digitizing rare editions and archival documents that are of scientific and historical value not only in their content, and in the notes of scientists in the margins of a book or archival document. Requirements for the quality of digitized materials determine the significant labor intensity of preparation; it is necessary to evaluate it when planning work on filling scientific libraries. The article presents a labor cost calculation methodology for creating integrated digital content for the digital library “Scientific Heritage of Russia” (DL SHR). The content of the DL SHR contains rare (out of print, hard-to find) books, 3D-museum objects and archival documents, which make digital copies of these materials very labour intensive. This needs to be assessed when planning the content filling for DL SHR. The developed technique includes the decomposition of the entire technological process into a number of operations performed by specialists of a certain profile (archivists, librarians, editors, scanners, etc.). Each phase is divided into several operations, and for every operation the time spent on this type of work is estimated. A unit of DL SHR content can be an archival document, a page of a book, a whole book, a biography of a scientist, etc. The assessment of the time period is carried out either according to published standards, or, in their absence, based on analysis of the experience of performing the operation when forming the content of the DL SHR. The article provides data on the calculation of time costs for individual operations of the formation of digital objects and their collections in relation to DL SHR, taking into account Russian standards and 15 years of experience.
Although being well-adopted and in widespread use, attribute-based access control (ABAC) remains a hard-to-master security paradigm in application software development. Despite considerable research towards ABAC policy engineering and ABAC policy correctness, this mainly is because there is still no unified workflow to encompass both the versatility of application domains and the strong guarantees promised by formal modeling methods. This work contributes to improving this situation. By presenting a flexible, yet highly formalized modeling scheme for designing and analyzing ABAC policies (DABAC), a reference implementation in Rust (dabac-rs), and a reference architecture for its integration into applications (AppSPEAR) including developer support (appspear-rs), we put together loose pieces of a tool-supported model-based security engineering workflow. The effectiveness of our approach is demonstrated based on a real-world engineering scenario.
Bottlenecks arise in many processes, often negatively impacting performance. Process mining can facilitate bottleneck analysis, but research has primarily focused on bottleneck detection and resolution, with limited attention given to the prediction of bottlenecks and recommendations for improving process performance. As a result, operational support for bottleneck resolution is often partially or not realized. The aim of this paper is to propose a method for Bottleneck Detection, Prediction, and Recommendation (BDPR) using process mining techniques to achieve operational support. A design science research methodology is adopted to design, develop, and demonstrate the BDPR method. A systematic literature review and a developed classification model provide theoretical support for the BDPR method and offer scholarly in the field of process mining a starting point for research. The BDPR method extends the utility of the classification model and aims to provide guidance to scholars and practitioners for assessing, selecting, evaluating, and implementing process mining techniques to realize operational support. A case study at a logistics service provider demonstrates the use of the proposed BDPR method.
Valuing invention disclosures in universities involves an assessment of research outcomes that go well beyond the opportunity to make financial profits. Valuation is also about determining what technical solutions are worth, either in terms of technical and scientific achievements but overall, what benefits does it bring to industry and the larger society. The good use of valuation methods to estimate and predict the outcomes of continuing to invest in the technical solution, is a major issue addressed by universities’ Technology transfer Offices (TTOs), which have to decide whether to file or not a patent to enforce industrial property rights and to gain the exclusivity to use and grant exploration rights over the new technical solution. University Technology Transfer Offices, in close connection with the research teams, must also make decisions regarding the technology roadmap and what avenues of investment will be used, and what valorisation routes will bring greater benefits to move the technology to industry where the results are developed into new products, compounds or systems or even services that benefit multiple stakeholders. This paper provides evidence on what are the main technology valuation methods in use by technology transfer offices. Qualitative and quantitative data has been collected by surveying eight Portuguese University TTOs. The statistical data has been treated using the one-sample T-test to identify the most common technology valuation methods. From the data it was possible to conclude that rating/ranking methods and models are the most frequently used valuation methods, followed by market valuation approaches. Previous agreements and discounted cash-flow projections are mainly used when a spin-off firm is under consideration or when there is a manifestation of interest from a potential investor. Royalty standards are used to prepare licensing negotiations, and Real Options, Monte Carlo simulation and Auctions are hardly ever used.
The combined adoption of selective encryption and smart contracts deployed on blockchains allows data owners to maintain control over their data when traded on digital data market platforms. Selective encryption, combined with key derivation techniques, guarantees that only customers who are entitled to access a resource can read its content. The adoption of smart contracts deployed on a blockchain permits to regulate the interplay among parties, the possible economic incentives to be paid to the owners, and the exchange of the information necessary for resource decryption (i.e., updates to the key derivation structure) upon payment. However, operations on blockchains have a cost. In this paper, we propose two approaches for updating the key derivation structure to enable customers to access resources, while limiting access times to resources and the cost of write operations on the blockchain to enforce purchases.
While there have been approaches for integrating security policies into operating systems (OSs) for more than two decades, applications often use objects of higher abstraction requiring individual security policies with application-specific semantics. Due to insufficient OS support, current approaches for enforcing application-level policies typically lead to large and complex trusted computing bases rendering tamperproofness and correctness difficult to achieve. To mitigate this problem, we propose the application-level policy enforcement architecture AppSPEAR and a C++ framework for its implementation. The configurable framework enables developers to balance enforcement rigor and costs imposed by different implementation alternatives and thus to easily tailor an AppSPEAR implementation to individual application requirements. We especially argue that hardware-based trusted execution environments offer an optimal balance between effectiveness and efficiency of policy protection and enforcement. This claim is substantiated by a practical evaluation based on an electronic medical record system.
Lawful interception, network forensics, and security incident investigations require cross-layer linking of identification information to link different activities of a particular person. This paper presents a model called graphs of identifiers that allows cross-layer linking of identifiers detected by various methods. Graphs of identifiers provide operations that link identifiers according to the constraints provided in the queries. The goal is to employ the linking during early stages of the network forensic investigations when an investigator searches for leads. The tools that implement the proposed model are publicly available.
Common for the overwhelming majority of privacy-preserving greater-than integer comparison schemes is that cryptographic computations are conducted in a bitwise manner. To ensure secrecy, each bit must be encoded in such a way that nothing is revealed to the opposite party. The most noted disadvantage is that the computational and communication cost of bitwise encoding is at best linear to the number of bits. Also, many proposed schemes have complex designs that may be difficult to implement. Carlton et al. (2018) proposed an interesting scheme that avoids bitwise decomposition and works on whole integers. A variant was proposed by Bourse et al. (2019). Despite that the stated adversarial model of these schemes is honest-but-curious users, we show that they are vulnerable to malicious users. Inspired by the two mentioned papers, we propose a novel comparison scheme, which is resistant to malicious users.
