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- Institut für Softwaretechnik (7) (remove)
Nowadays, almost any IT system involves personal data processing. In
such systems, many privacy risks arise when privacy concerns are not
properly addressed from the early phases of the system design. The
General Data Protection Regulation (GDPR) prescribes the Privacy by
Design (PbD) principle. As its core, PbD obliges protecting personal
data from the onset of the system development, by effectively
integrating appropriate privacy controls into the design. To
operationalize the concept of PbD, a set of challenges emerges: First, we need a basis to define privacy concerns. Without such a basis, we are not able to verify whether personal data processing is authorized. Second, we need to identify where precisely in a system, the controls have to be applied. This calls for system analysis concerning privacy concerns. Third, with a view to selecting and integrating appropriate controls, based on the results of system analysis, a mechanism to identify the privacy risks is required. Mitigating privacy risks is at the core of the PbD principle. Fourth, choosing and integrating appropriate controls into a system are complex tasks that besides risks, have to consider potential interrelations among privacy controls and the costs of the controls.
This thesis introduces a model-based privacy by design methodology to handle the above challenges. Our methodology relies on a precise definition of privacy concerns and comprises three sub-methodologies: model-based privacy analysis, modelbased privacy impact assessment and privacy-enhanced system design modeling. First, we introduce a definition of privacy preferences, which provides a basis to specify privacy concerns and to verify whether personal data processing is authorized. Second, we present a model-based methodology to analyze a system model. The results of this analysis denote a set of privacy design violations. Third, taking into account the results of privacy analysis, we introduce a model-based privacy impact assessment methodology to identify concrete privacy risks in a system model. Fourth, concerning the risks, and taking into account the interrelations and the costs of the controls, we propose a methodology to select appropriate controls and integrate them into a system design. Using various practical case studies, we evaluate our concepts, showing a promising outlook on the applicability of our methodology in real-world settings.
Software systems have an increasing impact on our daily lives. Many systems process sensitive data or control critical infrastructure. Providing secure software is therefore inevitable. Such systems are rarely being renewed regularly due to the high costs and effort. Oftentimes, systems that were planned and implemented to be secure, become insecure because their context evolves. These systems are connected to the Internet and therefore also constantly subject to new types of attacks. The security requirements of these systems remain unchanged, while, for example, discovery of a vulnerability of an encryption algorithm previously assumed to be secure requires a change of the system design. Some security requirements cannot be checked by the system’s design but only at run time. Furthermore, the sudden discovery of a security violation requires an immediate reaction to prevent a system shutdown. Knowledge regarding security best practices, attacks, and mitigations is generally available, yet rarely integrated part of software development or covering evolution.
This thesis examines how the security of long-living software systems can be preserved taking into account the influence of context evolutions. The goal of the proposed approach, S²EC²O, is to recover the security of model-based software systems using co-evolution.
An ontology-based knowledge base is introduced, capable of managing common, as well as system-specific knowledge relevant to security. A transformation achieves the connection of the knowledge base to the UML system model. By using semantic differences, knowledge inference, and the detection of inconsistencies in the knowledge base, context knowledge evolutions are detected.
A catalog containing rules to manage and recover security requirements uses detected context evolutions to propose potential co-evolutions to the system model which reestablish the compliance with security requirements.
S²EC²O uses security annotations to link models and executable code and provides support for run-time monitoring. The adaptation of running systems is being considered as is round-trip engineering, which integrates insights from the run time into the system model.
S²EC²O is amended by prototypical tool support. This tool is used to show S²EC²O’s applicability based on a case study targeting the medical information system iTrust.
This thesis at hand contributes to the development and maintenance of long-living software systems, regarding their security. The proposed approach will aid security experts: It detects security-relevant changes to the system context, determines the impact on the system’s security and facilitates co-evolutions to recover the compliance with the security requirements.
Absicherung der analytischen Interpretation von Geolokalisierungsdaten in der Mobilfunkforensik
(2019)
Abstract
Location based services maybe are within one of the most outstanding features of modern mobile devices. Despite the fact, that cached geolocation data could be used to reconstruct motion profiles, the amount of devices capable to provide these information in the field of criminal investigations is growing.
Motivation
The aim of this work is to generate in-depth knowledge to questions concerning geolocation in the field of mobile forensics, making especially somehow cached geolocation data forensically valuable. On top, tools meeting the specific requirements of law enforcement personnel shall be developed.
Problems
Geolocation processes within smartphones are quite complex. For the device to locate its position, different reference systems like GPS, cell towers or WiFi hot\-spots are used in a variety of ways. The whole mobile geolocation mechanism is proprietary to the device manufacturer and not build with forensic needs in mind. One major problem regarding forensic investigations is, that mainly reference points are being extracted and processed instead of real life device location data. In addition, these geolocation information only consist of bits and bytes or numeric values that have to be securely assigned to their intended meaning. The location data recovered are full of gaps providing only a part of the process or device usage. This possible loss of data has to be determined deriving a reliable measurement for the completeness, integrity and accuracy of data. Last but not least, as for every evidence within a criminal investigation, it has to be assured, that manipulations of the data or errors in position estimation have no disadvantageous effect on the analysis.
Research Questions
In the context of localisation services in modern smartphones, it always comes back to similar questions during forensic everyday life:
* Can locations be determined at any time?
* How accurate is the location of a smartphone?
* Can location data from smartphones endure in court?
Approach
For a better understanding of geolocation processes in modern smartphones and to evaluate the quality and reliability of the geolocation artefacts, information from different platforms shall be theoretically analysed as well as observed in-place during the geolocation process. The connection between data points and localisation context will be examined in predefined live experiments as well as desktop- and native applications on smartphones.
Results
Within the scope of this thesis self developed tools have been used for forensic investigations as well as analytical interpretation of geodata from modern smartphones. Hereby a generic model for assessing the quality of location data has emerged, which can be generally applied to geodata from mobile devices.
Within this thesis time evaluated predicate/transition nets (t-pr/t-nets) have been developed for the purpose to model, simulate and verify complex real-time systems. Therefore, t-pr/t-nets integrate concepts to model timing constraints and can be analysed by the means of structural analysis such as the calculation of s- and t-invariants as well as the identification of traps and co-traps. The applicability of t-pr/t-nets to model, simulate and verify complex systems in the domain of safety-critical real-time systems is proven by the Earliest-Deadline-First-Protocol (EDF) and the Priority-Inheritance-Protocol (PIP). Therefore, the EDF and PIP are modeled by means of t-pr/t-nets. The resulting t-pr/t-nets are verified using structural analysis methods. Due to the enormous complexity and the applicability of structural analysis methods for the verification of the EDF and PIP, it can be shown that t-pr/t-nets are appropriate to model, simulate and verify complex systems in the field of safety-critical real-time systems.
Probability propagation nets
(2008)
This work introduces a Petri net representation for the propagation of probabilities and likelihoods, which can be applied to probabilistic Horn abduction, fault trees, and Bayesian networks. These so-called "probability propagation nets" increase the transparency of propagation processes by integrating structural and dynamical aspects into one homogeneous representation. It is shown by means of popular examples that probability propagation nets improve the understanding of propagation processes - especially with respect to the Bayesian propagation algorithms - and thus are well suited for the analysis and diagnosis of probabilistic models. Representing fault trees with probability propagation nets transfers these possibilities to the modeling of technical systems.
Data-minimization and fairness are fundamental data protection requirements to avoid privacy threats and discrimination. Violations of data protection requirements often result from: First, conflicts between security, data-minimization and fairness requirements. Second, data protection requirements for the organizational and technical aspects of a system that are currently dealt with separately, giving rise to misconceptions and errors. Third, hidden data correlations that might lead to influence biases against protected characteristics of individuals such as ethnicity in decision-making software. For the effective assurance of data protection needs,
it is important to avoid sources of violations right from the design modeling phase. However, a model-based approach that addresses the issues above is missing.
To handle the issues above, this thesis introduces a model-based methodology called MoPrivFair (Model-based Privacy & Fairness). MoPrivFair comprises three sub-frameworks: First, a framework that extends the SecBPMN2 approach to allow detecting conflicts between security, data-minimization and fairness requirements. Second, a framework for enforcing an integrated data-protection management throughout the development process based on a business processes model (i.e., SecBPMN2 model) and a software architecture model (i.e., UMLsec model) annotated with data protection requirements while establishing traceability. Third, the UML extension UMLfair to support individual fairness analysis and reporting discriminatory behaviors. Each of the proposed frameworks is supported by automated tool support.
We validated the applicability and usability of our conflict detection technique based on a health care management case study, and an experimental user study, respectively. Based on an air traffic management case study, we reported on the applicability of our technique for enforcing an integrated data-protection management. We validated the applicability of our individual fairness analysis technique using three case studies featuring a school management system, a delivery management system and a loan management system. The results show a promising outlook on the applicability of our proposed frameworks in real-world settings.
Real-time operating systems for mixed-criticality systems
must support different types of software, such as
real-time applications and general purpose applications,
and, at the same time, must provide strong spatial and
temporal isolation between independent software components.
Therefore, state-of-the-art real-time operating systems
focus mainly on predictability and bounded worst-case behavior.
However, general purpose operating systems such as Linux
often feature more efficient---but less deterministic---mechanisms
that significantly improve the average execution time.
This thesis addresses the combination of the two contradicting
requirements and shows thread synchronization mechanisms
with efficient average-case behavior, but without sacrificing
predictability and worst-case behavior.
This thesis explores and evaluates the design space of fast paths
in the implementation of typical blocking synchronization
mechanisms, such as mutexes, condition variables, counting
semaphores, barriers, or message queues. The key technique here
is to avoid unnecessary system calls, as system calls have high
costs compared to other processor operations available in user
space, such as low-level atomic synchronization primitives.
In particular, the thesis explores futexes, the state-of-the-art
design for blocking synchronization mechanisms in Linux
that handles the uncontended case of thread synchronization
by using atomic operations in user space and calls into the
kernel only to suspend and wake up threads. The thesis also
proposes non-preemptive busy-waiting monitors that use an
efficient priority ceiling mechanism to prevent the lock holder
preemption problem without using system calls, and according
low-level kernel primitives to construct efficient wait and
notify operations.
The evaluation shows that the presented approaches
improve the average performance comparable
to state-of-the-art approaches in Linux.
At the same time, a worst-case timing analysis shows
that the approaches only need constant or bounded temporal
overheads at the operating system kernel level.
Exploiting these fast paths is a worthwhile approach
when designing systems that not only have to fulfill
real-time requirements, but also best-effort workloads.