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PDS2: A user-centered decentralized marketplace for privacy preserving data processing
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-0223-8907
Univ Cyprus, Dept Comp Sci, Nicosia, Cyprus..
Fdn Res & Technol Hellas, Inst Comp Sci, Iraklion, Greece..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
Show others and affiliations
2021 (English)In: 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW 2021), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 92-99Conference paper, Published paper (Refereed)
Abstract [en]

We envision PDS2, a decentralized data marketplace in which consumers submit their tasks to be run within the platform, on the data of willing providers. The goal of PDS2 is to ensure that users maintain full control on their data and do not compromise their privacy, while being rewarded for the value that their data generates. In order to achieve this, our marketplace architecture employs blockchain technology, privacypreserving computation and decentralized machine learning. We then compare different potential solutions and identify the Ethereum blockchain, trusted execution environments and gossip learning as the most suitable for the implementation of PDS2. We also discuss the main open challenges that are left to tackle and possible directions for future work.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 92-99
Series
IEEE International Conference on Data Engineering Workshop, ISSN 1943-2895
Keywords [en]
iot, blockchain, machine learning, privacy
National Category
Computer Sciences Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-300240DOI: 10.1109/ICDEW53142.2021.00024ISI: 000681131300018Scopus ID: 2-s2.0-85107689093OAI: oai:DiVA.org:kth-300240DiVA, id: diva2:1589025
Conference
37th IEEE International Conference on Data Engineering (IEEE ICDE), APR 19-22, 2021, ELECTR NETWORK
Note

Part of proceedings: ISBN 978-1-6654-4890-1, QC 20230117

Available from: 2021-08-30 Created: 2021-08-30 Last updated: 2023-05-17Bibliographically approved
In thesis
1. Towards Decentralized Graph Learning
Open this publication in new window or tab >>Towards Decentralized Graph Learning
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Current Machine Learning (ML) approaches typically present either a centralized or federated architecture. However, these architectures cannot easily keep up with some of the challenges introduced by recent trends, such as the growth in the number of IoT devices, increasing awareness about the privacy and security implications of extensive data collection, and the rise of graph-structured data and Graph Representation Learning. Systems based on either direct data collection or Federated Learning contain centralized, privileged systems that may act as scalability bottlenecks and dangerous single points of failure, while requiring users to trust the privacy protections and security practices in place. The combination of these issues ultimately leads to data waste, as opportunities to extract insights from available data are missed and thus the full societal benefits of advanced data analytics and ML are not realized.

In this thesis, we argue for a paradigm shift towards a completely decentralized and trustless architecture for privacy-aware Graph Representation Learning, which employs Gossip Learning and other gossip-based peer-to-peer techniques to achieve high levels of scalability and resilience while reducing the risk of privacy leaks. We then identify and pursue three key research directions necessary to achieve our vision: lifting unrealistic assumptions on Gossip Learning, identifying and developing specific use cases that are enabled or improved by gossip-based decentralization, and overcoming the obstacles to the deployment of decentralized training and inference for Graph Representation Learning models.

 Based on these key directions, our contributions are as follows. First, we analyze the robustness of Gossip Learning when several unrealistic but often assumed conditions are lifted. Then, we exploit Gossip Learning and gossip-based peer-to-peer protocols more in general across three use cases: the collaborative training of differentially-private Naive Bayes classifiers across organizations holding sensitive user data; the construction of decentralized, privacy-preserving data marketplaces; and the development and decentralization of early-stage IoT botnet detection systems based on Graph Representation Learning. Finally, we introduce a general framework for the fully-decentralized training of Graph Neural Networks, overcoming the typical requirement of these models to access non-local information during training and inference.

 The combination of these contributions removes major roadblocks towards decentralized graph learning, and also opens a new research direction aimed at further developing and optimizing the fully-decentralized training of Graph Representation Learning models.

Abstract [sv]

Dagens metoder för maskininlärning (ML) har vanligtvis antingen en centraliserad eller federerad arkitektur. Dessa arkitekturer kan dock inte lätt hålla jämna steg med några av de utmaningar som introducerats av de senaste trenderna, som till exempel ökningen av antalet IoT-enheter, ökad medvetenhet om integritets- och säkerhetskonsekvenserna av omfattande datainsamling samt ökningen av grafstrukturerad data och Graph Representation Learning. System baserade på antingen direkt datainsamling eller federerad inlärning innehåller centraliserade, privilegierade system som kan vara flaskhalsar och riskerar bli kritiska sårbarhetspunkter. Samtidigt måste användarna lita på integritetsskyddet och säkerhetspraxis som finns. Kombinationen av dessa problem leder i slutändan till ett ineffektivt nyttjande av data, eftersom möjligheter att utvinna insikter från tillgänglig data inte utnyttjas och därmed inte realiserar de fulla samhällsnyttorna som är möjliga med avancerad dataanalys och ML.

I denna avhandling argumenterar vi för ett paradigmskifte mot en helt decentraliserad och tillitslös arkitektur för integritetsmedveten Graph Representation Learning, som använder Gossip Learning och andra gossip-baserade peer-to-peer-tekniker för att uppnå höga nivåer av skalbarhet och motståndskraft, samtidigt som den minskar risken för integritetsläckor. Vi identifierar och driver sedan tre viktiga forskningsinriktningar som är nödvändiga för att uppnå vår vision; att lyfta orealistiska antaganden om Gossip Learning, identifiera och utveckla specifika användningsfall som möjliggörs eller förbättras av gossip-baserad decentralisering, samt övervinna hindren för utplacering av decentraliserad utbildning och inferens för Graph Representation Learning modeller.

Baserat på dessa nyckelriktlinjer våra bidrag är följande. Först analyserar vi robustheten i Gossip Learning när flera orealistiska men ofta antagna villkor upphävs. Vi utnyttjar sedan Gossip Learning och gossip-baserade peer-to-peer-protokoll mer generellt i tre användningsfall: kollaborativ inlärning av differentiellt privata Naive Bayes-klassificerare över entiteter med känslig användardata; byggandet av decentraliserade datamarknadsplatser som bevarar integriteten; samt utveckling och decentralisering av IoT-botnätdetekterings\-system i ett tidigt skede baserade på Graph Representation Learning. Slutligen introducerar vi ett allmänt ramverk för helt decentraliserad utbildning av Graph Neural Networks, som eliminerar de typiska kraven för dessa modeller för att få tillgång till icke-lokal information under träning och inferens.

Kombinationen av dessa bidrag tar bort stora hinder mot decentraliserad grafinlärning, och öppnar också en ny forskningsriktning som syftar till att vidareutveckla och optimera den helt decentraliserade utbildningen av Graph Representation Learning modeller.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. p. vii, 59
Series
TRITA-EECS-AVL ; 2023:42
National Category
Computer Sciences
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-327016 (URN)978-91-8040-584-3 (ISBN)
Public defence
2023-06-09, Sal-C, Kistagången 16, Stockholm, 09:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 813162
Note

QC 20230517

Available from: 2023-05-17 Created: 2023-05-17 Last updated: 2023-05-26Bibliographically approved

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