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Habilitation À Diriger Des Recherches Year : 2021

Large-scale trustworthy distributed collaboration


Most commonly used collaborative systems are provided by large service providers. While these collaborative services offer very interesting functionalities, they feature certain limitations. Most of the platforms hosting these collaboration services rely on a central authority and place personal information in the hands of a single large corporation which is a perceived privacy threat. Users must provide and store their data to vendors of these services and have to trust that they will preserve privacy of their data, but they have little control over the usage of their data after sharing it with other users. Moreover, these systems do not scale well in terms of the number of users and their modifications. Furthermore, user communities cannot deploy these kind of service applications since they generally rely on costly infrastructures rather than allowing sharing infrastructure and administration costs. My research work aims to move away from centralized authority-based collaboration towards a large scale trust-based peer-to-peer collaboration where control over data is given to users who can decide with whom to share their data. The main advantages of peer-to-peer collaborative systems are high scalability, resilience to faults and attacks and a low deployment barrier for new services. My contributions are structured around two axes of research: collaborative data management and trustworthy collaboration. In order to provide efficient data availability, data is typically replicated and users are allowed to concurrently modify replicated data. One of the challenges is to develop optimistic replication algorithms that maintain consistency of the shared data in the face of concurrent modifications. These algorithms have to be reliable, i.e. after the reception of all modifications the data copies have to converge. These algorithms also have to be scalable, i.e. have a complexity that does not depend on the number of users. These algorithms have also to be explainable, i.e. their decision must be comprehensible by users and therefore user intentions must be preserved. In the first part of this manuscript I present my contributions to the design of various optimistic data replication and their evaluation in terms of their complexities but also by means of experimental design with users. I also present my contributions on group awareness specifically on what information should be provided to users to prevent conflicting changes and to understand divergence when conflicts cannot be avoided. In this large scale peer-to-peer collaboration where users cannot remember the interactions with all collaborators a main question is how to choose the trusted collaborators in order to share them the data. A main challenge is how to assess users trust according to their past behaviour during collaboration in order to be able to predict their future behavior. In the second part of this manuscript I present a contract-based collaboration model where contracts are specified by the data owners when they share the data and user trust is assessed according to the observation of adherence to or violation of contracts. I also present a hash chain based authenticators approach for ensuring integrity and authenticity of logs of collaboration. For testing the proposed trust-based collaboration model, I designed a user experiment employing trust game and relying on a computational trust metric according to user exchanges in this game. I finally present my future research directions around secure and trustworthy collaborative data management.
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Dates and versions

tel-03229173 , version 1 (18-05-2021)


  • HAL Id : tel-03229173 , version 1


Claudia-Lavinia Ignat. Large-scale trustworthy distributed collaboration. Distributed, Parallel, and Cluster Computing [cs.DC]. Université de Lorraine, 2021. ⟨tel-03229173⟩
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