Agrégation de liens | EPFL Graph Search

333px|right|thumb|Une application de l'agrégation de liens. L'agrégation de liens est une technique utilisée dans les réseaux informatiques, permettant le regroupement de plusieurs ports réseau et de les utiliser comme s'il s'agissait d'un seul. Le but est d’accroitre le débit au-delà des limites d'un seul lien, et éventuellement de faire en sorte que les autres ports prennent le relai si un lien tombe en panne (redondance). Suivant le contexte, on peut rencontrer la notion d'agrégation de liens sous d'autres appellations : LAG (Link Aggregation Group), Shortest Path Bridging, trunk Ethernet, EtherChannel, NIC teaming, port channel, port teaming, port trunking, link bundling, multi-link trunking (MLT), NIC bonding, network bonding, bonding, network fault tolerance (NFT)… L'agrégation de liens est le plus souvent mise en place entre les ports de commutateurs Ethernet ou entre des cartes Ethernet d'ordinateurs sous Linux, Unix ou Windows. Toutefois, l'agrégation est un concept général

Privacy - Preserving Data Exchange and Aggregation in Healthcare

Alevtina Dubovitskaya

Medical data are often scattered among multiple clinics, hospitals, insurance companies, pharmacies, and research institutions that store and process personal healthcare information. The use of information and communication technologies for health (eHealth) provides us with the means to share healthcare data between authorized parties in an efficient manner. In this thesis, we address some of the challenges of implementing eHealth in practice: to achieve interoperability between data sources, and to ensure privacy for patients. Achieving both of these guarantees is our goal but they seem conflictual, hence the challenge. Once interoperability is achieved and a patientâs data are shared, it becomes evenmore difficult to ensure the patientâs privacy i.e., to provide to a patient control over his data and to guarantee the data anonymity in medical research. We address the aforementioned challenges by studying requirements from medical and legal perspectives, and by developing algorithms and frameworks to support privacy-preserving dynamic data-sharing, exchange, and aggregation from multiple data sources. In the first part of the thesis, we address certain privacy challenges. We present a framework based on the blockchain technology for ensuring traceability and accountability when sharing, exchanging, and aggregating medical data. Our framework ensures privacy, security, availability, and fine-grained access control over highly sensitive patient-data. We also analyze the potential of applying blockchain technology in different eHealth settings: primary care, medical-data research, and connected health. Our second contribution is a framework for privacy-preserving data aggregation: an algorithm for constructing the anonymized database and a protocol that improves the utility of the anonymized database as the database grows. In the second part of the thesis, we focus on achieving interoperability. We design an interface specification that defines communication protocols andmessages supporting integration of a new software tool in clinical practice. Then, we develop a multi-agent system (MAS) for the dynamic aggregation of the data collected and generated by this software tool for the purpose of clinical research. This MAS takes into account the objectives of the research study, the availability of data, and could employ our proposed algorithm for privacy-preserving data aggregation. The negotiation protocol in the framework of theMAS achieves a precise definition of database characteristics, such as schema, content, and privacy parameters, therefore increasing the efficiency of data collection for medical research and ensuring the privacy of patients.

EPFL2018

A Multiagent System for Dynamic Data Aggregation in Medical Research

Karl Aberer, Alevtina Dubovitskaya, Michael Ignaz Schumacher

The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research.

Hindawi Publishing Corporation2016

An Agent Framework for Dynamic Health Data Aggregation for Research Purposes

Karl Aberer, Alevtina Dubovitskaya, Michael Ignaz Schumacher

This paper presents a model of a MAS framework for dynamic aggregation of population health data for research purposes. The contribution of the paper is twofold: First, it describes a MAS architecture that allows one to built on the fly anonymized databases from the distributed sources of data. Second, it shows how to improve the utility of the data with the growth of the database.

2015

Privacy - Preserving Data Exchange and Aggregation in Healthcare

Alevtina Dubovitskaya

EPFL2018