881 research outputs found
Privacy-preserving data sharing infrastructures for medical research: systematization and comparison
Background: Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches.
Methods: The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes.
Results: Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility.
Conclusions: There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken
Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective
Rapid advances in human genomics are enabling researchers to gain a better
understanding of the role of the genome in our health and well-being,
stimulating hope for more effective and cost efficient healthcare. However,
this also prompts a number of security and privacy concerns stemming from the
distinctive characteristics of genomic data. To address them, a new research
community has emerged and produced a large number of publications and
initiatives.
In this paper, we rely on a structured methodology to contextualize and
provide a critical analysis of the current knowledge on privacy-enhancing
technologies used for testing, storing, and sharing genomic data, using a
representative sample of the work published in the past decade. We identify and
discuss limitations, technical challenges, and issues faced by the community,
focusing in particular on those that are inherently tied to the nature of the
problem and are harder for the community alone to address. Finally, we report
on the importance and difficulty of the identified challenges based on an
online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies
(PoPETs), Vol. 2019, Issue
Redescribing Health Privacy: The Importance of Health Policy
Current conversations about health information policy often tend to be based on three broad assumptions. First, many perceive a tension between regulation and innovation. We often hear that privacy regulations are keeping researchers, companies, and providers from aggregating the data they need to promote innovation. Second, aggregation of fragmented data is seen as a threat to its proper regulation, creating the risk of breaches and other misuse. Third, a prime directive for technicians and policymakers is to give patients ever more granular methods of control over data. This article questions and complicates those assumptions, which I deem (respectively) the Privacy Threat to Research, the Aggregation Threat to Privacy, and the Control Solution.
This article is also intended to enrich our concepts of “fragmentation” and “integration” in health care. There is a good deal of sloganeering around “firewalls” and “vertical integration” as idealized implementations of “fragmentation” and “integration” (respective). The problem, though, is that terms like these (as well as “disruption”) are insufficiently normative to guide large-scale health system change. They describe, but they do not adequately prescribe.
By examining those instances where: a) regulation promotes innovation, and b) increasing (some kinds of) availability of data actually enhances security, confidentiality, and privacy protections, this article attempts to give a richer account of the ethics of fragmentation and integration in the U.S. health care system. But, it also has a darker side, highlighting the inevitable conflicts of values created in a “reputation society” driven by stigmatizing social sorting systems. Personal data control may exacerbate social inequalities. Data aggregation may increase both our powers of research and our vulnerability to breach. The health data policymaking landscape of the next decade will feature a series of intractable conflicts between these important social values
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
The vision of the upcoming 6G technologies, characterized by ultra-dense
network, low latency, and fast data rate is to support Pervasive AI (PAI) using
zero-touch solutions enabling self-X (e.g., self-configuration,
self-monitoring, and self-healing) services. However, the research on 6G is
still in its infancy, and only the first steps have been taken to conceptualize
its design, investigate its implementation, and plan for use cases. Toward this
end, academia and industry communities have gradually shifted from theoretical
studies of AI distribution to real-world deployment and standardization. Still,
designing an end-to-end framework that systematizes the AI distribution by
allowing easier access to the service using a third-party application assisted
by a zero-touch service provisioning has not been well explored. In this
context, we introduce a novel platform architecture to deploy a zero-touch
PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart
system. This platform aims to standardize the pervasive AI at all levels of the
architecture and unify the interfaces in order to facilitate the service
deployment across application and infrastructure domains, relieve the users
worries about cost, security, and resource allocation, and at the same time,
respect the 6G stringent performance requirements. As a proof of concept, we
present a Federated Learning-as-a-service use case where we evaluate the
ability of our proposed system to self-optimize and self-adapt to the dynamics
of 6G networks in addition to minimizing the users' perceived costs.Comment: IEEE Communications Magazin
Analysis and improvement of security and privacy techniques for genomic information
The purpose of this thesis is to review the current literature of privacy preserving techniques for genomic information on the last years. Based on the analysis, we propose a long-term classification system for the reviewed techniques. We also develop a security improvement proposal for the Beacon system without hindering research utility
Applying Formal Methods to Networking: Theory, Techniques and Applications
Despite its great importance, modern network infrastructure is remarkable for
the lack of rigor in its engineering. The Internet which began as a research
experiment was never designed to handle the users and applications it hosts
today. The lack of formalization of the Internet architecture meant limited
abstractions and modularity, especially for the control and management planes,
thus requiring for every new need a new protocol built from scratch. This led
to an unwieldy ossified Internet architecture resistant to any attempts at
formal verification, and an Internet culture where expediency and pragmatism
are favored over formal correctness. Fortunately, recent work in the space of
clean slate Internet design---especially, the software defined networking (SDN)
paradigm---offers the Internet community another chance to develop the right
kind of architecture and abstractions. This has also led to a great resurgence
in interest of applying formal methods to specification, verification, and
synthesis of networking protocols and applications. In this paper, we present a
self-contained tutorial of the formidable amount of work that has been done in
formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial
Analyzing the Impacts of Emerging Technologies on Workforce Skills: A Case Study of Industrial Engineering in the Context of the Industrial Internet of Things
New technologies can result in major disruptions and change paradigms that were once well established. Methods have been developed to forecast new technologies and to analyze the impacts of them in terms of processes, products, and services. However, the current literature does not provide answers on how to forecast changes in terms of skills and knowledge, given an emerging technology. This thesis aims to fill this literature gap by developing a structured method to forecast the required set of skills for emerging technologies and to compare it with the current skills of the workforce. The method relies on the breakdown of the emerging technology into smaller components, so then skills can be identified for each component. A case study was conducted to implement and test the proposed method. In this case study, the impacts of the Industrial Internet of Things (IIoT) on engineering skills and knowledge were assessed. Text data analytics validated IIoT as an emerging technology, thus justifying the case study based on engineering and manufacturing discussions. The set of skills required for IIoT was compared to the current skills developed by Industrial Engineering students at the University of Windsor. Text data analytics was also used to evaluate the importance of each IIoT component by measuring how associated individual components are to IIoT. Therefore, existing skill gaps between the current Industrial Engineering program and IIoT requirements were not only mapped, but they were also given weights
Towards Improved Homomorphic Encryption for Privacy-Preserving Deep Learning
Mención Internacional en el título de doctorDeep Learning (DL) has supposed a remarkable transformation for many fields, heralded
by some as a new technological revolution. The advent of large scale models has increased
the demands for data and computing platforms, for which cloud computing has become
the go-to solution. However, the permeability of DL and cloud computing are reduced
in privacy-enforcing areas that deal with sensitive data. These areas imperatively call for
privacy-enhancing technologies that enable responsible, ethical, and privacy-compliant
use of data in potentially hostile environments.
To this end, the cryptography community has addressed these concerns with what
is known as Privacy-Preserving Computation Techniques (PPCTs), a set of tools that
enable privacy-enhancing protocols where cleartext access to information is no longer
tenable. Of these techniques, Homomorphic Encryption (HE) stands out for its ability
to perform operations over encrypted data without compromising data confidentiality or
privacy. However, despite its promise, HE is still a relatively nascent solution with efficiency
and usability limitations. Improving the efficiency of HE has been a longstanding
challenge in the field of cryptography, and with improvements, the complexity of the
techniques has increased, especially for non-experts.
In this thesis, we address the problem of the complexity of HE when applied to DL.
We begin by systematizing existing knowledge in the field through an in-depth analysis
of state-of-the-art for privacy-preserving deep learning, identifying key trends, research
gaps, and issues associated with current approaches. One such identified gap lies in the
necessity for using vectorized algorithms with Packed Homomorphic Encryption (PaHE),
a state-of-the-art technique to reduce the overhead of HE in complex areas. This thesis
comprehensively analyzes existing algorithms and proposes new ones for using DL with
PaHE, presenting a formal analysis and usage guidelines for their implementation.
Parameter selection of HE schemes is another recurring challenge in the literature,
given that it plays a critical role in determining not only the security of the instantiation
but also the precision, performance, and degree of security of the scheme. To address
this challenge, this thesis proposes a novel system combining fuzzy logic with linear
programming tasks to produce secure parametrizations based on high-level user input
arguments without requiring low-level knowledge of the underlying primitives.
Finally, this thesis describes HEFactory, a symbolic execution compiler designed to
streamline the process of producing HE code and integrating it with Python. HEFactory
implements the previous proposals presented in this thesis in an easy-to-use tool. It provides
a unique architecture that layers the challenges associated with HE and produces
simplified operations interpretable by low-level HE libraries. HEFactory significantly reduces
the overall complexity to code DL applications using HE, resulting in an 80% length
reduction from expert-written code while maintaining equivalent accuracy and efficiency.El aprendizaje profundo ha supuesto una notable transformación para muchos campos
que algunos han calificado como una nueva revolución tecnológica. La aparición de modelos
masivos ha aumentado la demanda de datos y plataformas informáticas, para lo cual,
la computación en la nube se ha convertido en la solución a la que recurrir. Sin embargo,
la permeabilidad del aprendizaje profundo y la computación en la nube se reduce en los
ámbitos de la privacidad que manejan con datos sensibles. Estas áreas exigen imperativamente
el uso de tecnologías de mejora de la privacidad que permitan un uso responsable,
ético y respetuoso con la privacidad de los datos en entornos potencialmente hostiles.
Con este fin, la comunidad criptográfica ha abordado estas preocupaciones con las
denominadas técnicas de la preservación de la privacidad en el cómputo, un conjunto de
herramientas que permiten protocolos de mejora de la privacidad donde el acceso a la información
en texto claro ya no es sostenible. Entre estas técnicas, el cifrado homomórfico
destaca por su capacidad para realizar operaciones sobre datos cifrados sin comprometer
la confidencialidad o privacidad de la información. Sin embargo, a pesar de lo prometedor
de esta técnica, sigue siendo una solución relativamente incipiente con limitaciones
de eficiencia y usabilidad. La mejora de la eficiencia del cifrado homomórfico en la
criptografía ha sido todo un reto, y, con las mejoras, la complejidad de las técnicas ha
aumentado, especialmente para los usuarios no expertos.
En esta tesis, abordamos el problema de la complejidad del cifrado homomórfico
cuando se aplica al aprendizaje profundo. Comenzamos sistematizando el conocimiento
existente en el campo a través de un análisis exhaustivo del estado del arte para el aprendizaje
profundo que preserva la privacidad, identificando las tendencias clave, las lagunas
de investigación y los problemas asociados con los enfoques actuales. Una de las
lagunas identificadas radica en el uso de algoritmos vectorizados con cifrado homomórfico
empaquetado, que es una técnica del estado del arte que reduce el coste del cifrado
homomórfico en áreas complejas. Esta tesis analiza exhaustivamente los algoritmos existentes
y propone nuevos algoritmos para el uso de aprendizaje profundo utilizando cifrado
homomórfico empaquetado, presentando un análisis formal y unas pautas de uso para su
implementación.
La selección de parámetros de los esquemas del cifrado homomórfico es otro reto recurrente
en la literatura, dado que juega un papel crítico a la hora de determinar no sólo la
seguridad de la instanciación, sino también la precisión, el rendimiento y el grado de seguridad del esquema. Para abordar este reto, esta tesis propone un sistema innovador que
combina la lógica difusa con tareas de programación lineal para producir parametrizaciones
seguras basadas en argumentos de entrada de alto nivel sin requerir conocimientos
de bajo nivel de las primitivas subyacentes.
Por último, esta tesis propone HEFactory, un compilador de ejecución simbólica diseñado
para agilizar el proceso de producción de código de cifrado homomórfico e integrarlo
con Python. HEFactory es la culminación de las propuestas presentadas en esta
tesis, proporcionando una arquitectura única que estratifica los retos asociados con el
cifrado homomórfico, produciendo operaciones simplificadas que pueden ser interpretadas
por bibliotecas de bajo nivel. Este enfoque permite a HEFactory reducir significativamente
la longitud total del código, lo que supone una reducción del 80% en la
complejidad de programación de aplicaciones de aprendizaje profundo que usan cifrado
homomórfico en comparación con el código escrito por expertos, manteniendo una precisión
equivalente.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: María Isabel González Vasco.- Secretario: David Arroyo Guardeño.- Vocal: Antonis Michala
Decentralisation: a multidisciplinary perspective
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