4,588 research outputs found
Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review
Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds
Towards A Practical High-Assurance Systems Programming Language
Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation.
Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code.
To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Towards Fast and Scalable Private Inference
Privacy and security have rapidly emerged as first order design constraints.
Users now demand more protection over who can see their data (confidentiality)
as well as how it is used (control). Here, existing cryptographic techniques
for security fall short: they secure data when stored or communicated but must
decrypt it for computation. Fortunately, a new paradigm of computing exists,
which we refer to as privacy-preserving computation (PPC). Emerging PPC
technologies can be leveraged for secure outsourced computation or to enable
two parties to compute without revealing either users' secret data. Despite
their phenomenal potential to revolutionize user protection in the digital age,
the realization has been limited due to exorbitant computational,
communication, and storage overheads.
This paper reviews recent efforts on addressing various PPC overheads using
private inference (PI) in neural network as a motivating application. First,
the problem and various technologies, including homomorphic encryption (HE),
secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are
introduced. Next, a characterization of their overheads when used to implement
PI is covered. The characterization motivates the need for both GCs and HE
accelerators. Then two solutions are presented: HAAC for accelerating GCs and
RPU for accelerating HE. To conclude, results and effects are shown with a
discussion on what future work is needed to overcome the remaining overheads of
PI.Comment: Appear in the 20th ACM International Conference on Computing
Frontier
Technology for Low Resolution Space Based RSO Detection and Characterisation
Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment
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The impact of employees' working relations in creating and retaining trust: the case of the Bahrain Olympic Committee
Introduction: This thesis investigates the impact of employees’ working relations in creating, maintaining and retaining trust in the Bahrain Olympic Committee (BOC).
Aim: The main aim of this thesis is to determine how the three groups of Organisational Trust variables, namely Social System Elements (SSE), Factors of Trustworthiness (FoT) and Third-Party Gossip (TPG), affect employees’ Organisational Trust (OTR) in the BOC and promote Organisational Citizenship Behaviour (OCB). To answer this main aim, a conceptual framework was created that focused on exploring the following research aims: (1) the interrelationship between SSE and FoT, (2) the effect of SSE on OTR, (3) the impact of TPG on OTR and (4) the effect of OTR on overall OCB.
Methodology: The study uses a mixed-method case study research style that included in-depth semi-structured interviews with 17 managers, an online questionnaire survey with 320 employees of the BOC and an analysis of the BOC’s Annual Reports from 2015 to 2018.
Results: The qualitative and quantitative findings indicate, firstly, that there is a significant interrelationship between SSE and FoT, establishing that SSE’s perception of organisational justice (OJ), including that FoTs benevolence and integrity as the most important factors in yielding employees’ trust in the BOC. Secondly, it has been established that SSEs have significant direct and indirect effects on OTR. Thirdly, negative and positive TPG concurrently occurred in the BOC and the prevalence of negative TPG poses more impact on OTR. Finally, this study’s findings demonstrated OTR’s effect in generating OCB, including that Civic Virtue was rated as the most preferred of the five OCB themes; this indicates the managers’ and the employees’ strong emotional attachment and support of the activities taking place at the BOC.
Contributions: Overall, this thesis substantially contributes to OTR literature, particularly in the context of the Middle East. It also proposes several insightful recommendations for future research and practical implications for practitioners in the field of Organisational Trust
The Applicability of Federated Learning to Official Statistics
This work investigates the potential of Federated Learning (FL) for official
statistics and shows how well the performance of FL models can keep up with
centralized learning methods. At the same time, its utilization can safeguard
the privacy of data holders, thus facilitating access to a broader range of
data and ultimately enhancing official statistics. By simulating three
different use cases, important insights on the applicability of the technology
are gained. The use cases are based on a medical insurance data set, a fine
dust pollution data set and a mobile radio coverage data set - all of which are
from domains close to official statistics. We provide a detailed analysis of
the results, including a comparison of centralized and FL algorithm
performances for each simulation. In all three use cases, we were able to train
models via FL which reach a performance very close to the centralized model
benchmarks. Our key observations and their implications for transferring the
simulations into practice are summarized. We arrive at the conclusion that FL
has the potential to emerge as a pivotal technology in future use cases of
official statistics
Privacy-preserving patient clustering for personalized federated learning
Federated Learning (FL) is a machine learning framework that enables multiple
organizations to train a model without sharing their data with a central
server. However, it experiences significant performance degradation if the data
is non-identically independently distributed (non-IID). This is a problem in
medical settings, where variations in the patient population contribute
significantly to distribution differences across hospitals. Personalized FL
addresses this issue by accounting for site-specific distribution differences.
Clustered FL, a Personalized FL variant, was used to address this problem by
clustering patients into groups across hospitals and training separate models
on each group. However, privacy concerns remained as a challenge as the
clustering process requires exchange of patient-level information. This was
previously solved by forming clusters using aggregated data, which led to
inaccurate groups and performance degradation. In this study, we propose
Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel
Clustered FL framework that can cluster patients using patient-level data while
protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic
technique, to securely calculate patient-level similarity scores across
hospitals. We then evaluate PCBFL by training a federated mortality prediction
model using 20 sites from the eICU dataset. We compare the performance gain
from PCBFL against traditional and existing Clustered FL frameworks. Our
results show that PCBFL successfully forms clinically meaningful cohorts of
low, medium, and high-risk patients. PCBFL outperforms traditional and existing
Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC
improvement of 7.8%
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