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Girdzijauskas, SarunasORCID iD iconorcid.org/0000-0003-4516-7317
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Publications (10 of 14) Show all publications
Bahri, L. & Girdzijauskas, S. (2019). Blockchain technology: Practical P2P computing (Tutorial). In: Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019: . Paper presented at June 2019, Article number 8791982, Pages 249-2504th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019; Umea; Sweden; 16 June 2019 through 20 June 2019 (pp. 249-250). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8791982.
Open this publication in new window or tab >>Blockchain technology: Practical P2P computing (Tutorial)
2019 (English)In: Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 249-250, article id 8791982Conference paper, Published paper (Refereed)
Abstract [en]

Blockchain technology comes with the promise to revolutionize the way current IT systems are organized as well as to revise how trust is perceived in the wider society. In spite of the wide attention that cyrpto-currencies (such as Bitcoin) have attracted, Blockchain technology is more likely to make an impact beyond ongoing speculations on cyrpto-currencies. Decentralized identity management, transparent supply-chain systems, and IoT governance and security are only few examples of research challenges for which this technology may hold substantial potential. Blockchain technology has emerged at the intersection of two well established research areas: peer-to-peer (P2P) computing and cryptography. In this tutorial, we provide a general overview of the main components behind this technology, we present the difference between the types of Blockchain available today, and we make a high level discussion on its potentials and limitations as well as possible research challenges.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Blockchain, Consensus in Blockchain, DLT, PoW
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-262644 (URN)10.1109/FAS-W.2019.00066 (DOI)2-s2.0-85071419745 (Scopus ID)9781728124063 (ISBN)
Conference
June 2019, Article number 8791982, Pages 249-2504th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019; Umea; Sweden; 16 June 2019 through 20 June 2019
Note

QC 20191017

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-17Bibliographically approved
Giaretta, L. & Girdzijauskas, S. (2019). Gossip Learning: Off the Beaten Path. In: : . Paper presented at 2019 IEEE International Conference on Big Data (IEEE Big Data 2019), December 9-12, 2019, Los Angeles, CA, USA.
Open this publication in new window or tab >>Gossip Learning: Off the Beaten Path
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The growing computational demands of model training tasks and the increased privacy awareness of consumers call for the development of new techniques in the area of machine learning. Fully decentralized approaches have been proposed, but are still in early research stages. This study analyses gossip learning, one of these state-of-the-art decentralized machine learning protocols, which promises high scalability and privacy preservation, with the goal of assessing its applicability to realworld scenarios.

Previous research on gossip learning presents strong and often unrealistic assumptions on the distribution of the data, the communication speeds of the devices and the connectivity among them. Our results show that lifting these requirements can, in certain scenarios, lead to slow convergence of the protocol or even unfair bias in the produced models. This paper identifies the conditions in which gossip learning can and cannot be applied, and introduces extensions that mitigate some of its limitations.

National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-263863 (URN)
Conference
2019 IEEE International Conference on Big Data (IEEE Big Data 2019), December 9-12, 2019, Los Angeles, CA, USA
Note

Accepted paper. QC 20191122

Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2019-11-22Bibliographically approved
Apolonia, N., Freitag, F., Navarro, L. & Girdzijauskas, S. (2019). Socially aware microcloud service overlay optimization in community networks. Software, practice & experience, 49(1), Article ID 13.
Open this publication in new window or tab >>Socially aware microcloud service overlay optimization in community networks
2019 (English)In: Software, practice & experience, ISSN 0038-0644, Vol. 49, no 1, article id 13Article in journal (Refereed) Published
Abstract [en]

Community networks are a growing network cooperation effort by citizens to build and maintain Internet infrastructure in regions that are not available. Adding that, to bring cloud services to community networks (CNs), microclouds were started as an edge cloud computing model where members cooperate using resources. Therefore, enhancing routing for services in CNs is an attractive paradigm that benefits the infrastructure. The problem is the growing consumption of resources for disseminating messages in the CN environment. This is because the services that build their overlay networks are oblivious to the underlying workload patterns that arise from social cooperation in CNs. In this paper, we propose Select in Community Networks (SELECTinCN), which enhances the overlay creation for pub/sub systems over peer‐to‐peer (P2P) networks. Moreover, SELECTinCN includes social information based on cooperation within CNs by exploiting the social aspects of the community of practice. Our work organizes the peers in a ring topology and provides an adaptive P2P connection establishment algorithm, where each peer identifies the number of connections needed based on the social structure and user availability. This allows us to propagate messages using a reduced number of hops, thus providing an efficient heuristic to an NP‐hard problem that maps the workload graph to the structured P2P overlays resulting in a number of messages close to the theoretical minimum. Experiments show that, by using social network information, SELECTinCN reduces the number of relay nodes by up to 89% using the community of practice information versus the state‐of‐the‐art pub/sub notification systems given as baseline.

Place, publisher, year, edition, pages
Wiley: John Wiley & Sons, 2019
Keywords
community networks, community of practice, microclouds, P2P overlay networks, social networks
National Category
Communication Systems
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-259338 (URN)10.1002/spe.2750 (DOI)000485481400001 ()2-s2.0-85072199534 (Scopus ID)
Note

QC 20190916

Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-10-01Bibliographically approved
Bahri, L. & Girdzijauskas, S. (2019). Trust Mends Blockchains: Living up to Expectations. In: IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, July 7-10 2019: . Paper presented at IEEE 39th International Conference on Distributed Computing Systems - ICDCS 2019.
Open this publication in new window or tab >>Trust Mends Blockchains: Living up to Expectations
2019 (English)In: IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, July 7-10 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

At the heart of Blockchains is the trustless leader election mechanism for achieving consensus among pseudoanonymous peers, without the need of oversight from any third party or authority whatsoever. So far, two main mechanisms are being discussed: proof-of-work (PoW) and proof-of-stake (PoS). PoW relies on demonstration of computational power, and comes with the markup of huge energy wastage in return of the stake in cyrpto-currency. PoS tries to address this by relying on owned stake (i.e., amount of crypto-currency) in the system. In both cases, Blockchains are limited to systems with financial basis. This forces non-crypto-currency Blockchain applications to resort to “permissioned” setting only, effectively centralizing the system. However, non-crypto-currency permisionless blockhains could enable secure and self-governed peer-to-peer structures for numerous emerging application domains, such as education and health, where some trust exists among peers. This creates a new possibility for valuing trust among peers and capitalizing it as the basis (stake) for reaching consensus. In this paper we show that there is a viable way for permisionless non-financial Blockhains to operate in completely decentralized environments and achieve leader election through proof-of-trust (PoT). In our PoT construction, peer trust is extracted from a trust network that emerges in a decentralized manner and is used as a waiver for the effort to be spent for PoW, thus dramatically reducing total energy expenditure of the system. Furthermore, our PoT construction is resilient to the risk of small cartels monopolizing the network (as it happens with the mining-pool phenomena in PoW) and is not vulnerable to sybils. We evluate security guarantees, and perform experimental evaluation of our construction, demonstrating up to 10-fold energy savings compared to PoW without trading off any of the decentralization characteristics, with further guarantees against risks of monopolization.

Keywords
Proof-of-Trust Blockchain, Blockchain, PoW, PoT
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-251639 (URN)
Conference
IEEE 39th International Conference on Distributed Computing Systems - ICDCS 2019
Note

QC 20190521

Available from: 2019-05-16 Created: 2019-05-16 Last updated: 2019-05-21Bibliographically approved
Kefato, Z. T., Sheikh, N., Bahri, L., Soliman, A., Montresor, A. & Girdzijauskas, S. (2018). CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks. In: : . Paper presented at 2018 FIFTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS) (pp. 72-79). IEEE
Open this publication in new window or tab >>CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Effectively predicting whether a given post or tweet is going to become viral in online social networks is of paramount importance for several applications, such as trend and break-out forecasting. While several attempts towards this end exist, most of the current approaches rely on features extracted from the underlying network structure over which the content spreads. Recent studies have shown, however, that prediction can be effectively performed with very little structural information about the network, or even with no structural information at all. In this study we propose a novel network-agnostic approach called CAS2VEC, that models information cascades as time series and discretizes them using time slices. For the actual prediction task we have adopted a technique from the natural language processing community. The particular choice of the technique is mainly inspired by an empirical observation on the strong similarity between the distribution of discretized values occurrence in cascades and words occurrence in natural language documents. Thus, thanks to such a technique for sentence classification using convolutional neural networks, CAS2VEC can predict whether a cascade is going to become viral or not. We have performed extensive experiments on two widely used real-world datasets for cascade prediction, that demonstrate the effectiveness of our algorithm against strong baselines.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Software Engineering
Identifiers
urn:nbn:se:kth:diva-252430 (URN)10.1109/SNAMS.2018.8554730 (DOI)000466979100012 ()2-s2.0-85060045802 (Scopus ID)
Conference
2018 FIFTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS)
Note

QC 20190715

Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-07-15Bibliographically approved
Kefato, Z., Sheikh, N., Bahri, L., Soliman, A., Girdzijauskas, S. & Montresor, A. (2018). CaTS: Network-Agnostic Virality Prediction Model to Aid Rumour Detection. In: : . Paper presented at International Workshop on Rumours and Deception in Social Media (RDSM 2018).
Open this publication in new window or tab >>CaTS: Network-Agnostic Virality Prediction Model to Aid Rumour Detection
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2018 (English)Conference paper, Published paper (Refereed)
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-239543 (URN)
Conference
International Workshop on Rumours and Deception in Social Media (RDSM 2018)
Note

QC 20181130

Available from: 2018-11-26 Created: 2018-11-26 Last updated: 2018-11-30Bibliographically approved
Sozinov, K., Vlassov, V. & Girdzijauskas, S. (2018). Human Activity Recognition Using Federated Learning. In: Chen, JJ Yang, LT (Ed.), 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS: . Paper presented at 16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA (pp. 1103-1111). IEEE COMPUTER SOC
Open this publication in new window or tab >>Human Activity Recognition Using Federated Learning
2018 (English)In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1103-1111Conference paper, Published paper (Refereed)
Abstract [en]

State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. The global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Symposium on Parallel and Distributed Processing with Applications, ISSN 2158-9178
Keywords
Federated Learning, Human Activity Recognition, Privacy, Distributed Machine Learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-252673 (URN)10.1109/BDCloud.2018.00164 (DOI)000467843200150 ()2-s2.0-85063888507 (Scopus ID)978-1-7281-1141-4 (ISBN)
Conference
16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-06-03Bibliographically approved
Ghoorchian, K. & Girdzijauskas, S. (2018). Spatio-Temporal Multiple Geo-Location Identification on Twitter. In: Abe, N Liu, H Pu, C Hu, X Ahmed, N Qiao, M Song, Y Kossmann, D Liu, B Lee, K Tang, J He, J Saltz, J (Ed.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018: . Paper presented at 2018 IEEE International Conference on Big Data, Big Data 2018; Seattle; United States; 10 December 2018 through 13 December 2018 (pp. 3412-3421). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Spatio-Temporal Multiple Geo-Location Identification on Twitter
2018 (English)In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 / [ed] Abe, N Liu, H Pu, C Hu, X Ahmed, N Qiao, M Song, Y Kossmann, D Liu, B Lee, K Tang, J He, J Saltz, J, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3412-3421Conference paper, Published paper (Refereed)
Abstract [en]

Twitter Geo-tags that indicate the exact location of messages have many applications from localized opinion mining during elections to efficient traffic management in critical situations. However, less than 6% of Tweets are Geo-tagged, which limits the implementation of those applications. There are two groups of solutions: content and network-based. The first group uses location indicative factors like URLs and topics, extracted from the content of tweets, to infer Geo-location for non geoactive users, whereas the second group benefits from friendship ties in the underlying social network graph. Friendship ties are better predictors compared to content information because they are less noisy and often follow the natural human spatial movement patterns. However, their prediction's accuracy is still limited because they ignore the temporal aspects of human behavior and always assume a single location per user. This research aims to extend the current network-based approaches by taking users' temporal dimension into account. We assume multiple locations per user during different time-slots and hypothesize that location predictability varies depending on the time and the properties of the social membership group. Thus, we propose a hierarchical solution to apply temporal categorizations on top of social network partitioning for multiple location prediction for users in Online Social Networks (OSNs) like Twitter. Given a largescale Twitter dataset, we show that users' location predictability exhibits different behavior in different time-slots and different social groups. We find that there are specific conditions where users are more predictable in terms of Geo-location. Our solution outperforms the state-of-the-art by improving the prediction accuracy by 16:6% in terms of Median Error Distance (MED) over the same recall.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE International Conference on Big Data, ISSN 2639-1589
Keywords
Geo-Location Identification, Graph Partitioning, Social Network Analysis, Spatio-Temporal Analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-254147 (URN)10.1109/BigData.2018.8621997 (DOI)000468499303064 ()2-s2.0-85062605032 (Scopus ID)978-1-5386-5035-6 (ISBN)
Conference
2018 IEEE International Conference on Big Data, Big Data 2018; Seattle; United States; 10 December 2018 through 13 December 2018
Note

QC 20190624

Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-11-19Bibliographically approved
Soliman, A., Rahimian, F. & Girdzijauskas, S. (2018). Stad: Stateful Diffusion for Linear Time Community Detection. In: 38th IEEE International Conference on Distributed Computing Systems: . Paper presented at 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018; Vienna University of Technology (TU Wien)Vienna; Austria; 2 July 2018 through 5 July 2018.
Open this publication in new window or tab >>Stad: Stateful Diffusion for Linear Time Community Detection
2018 (English)In: 38th IEEE International Conference on Distributed Computing Systems, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to “one size fits all” model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions. In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with a conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping “one size fits all” model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

Keywords
Community Detection, Flow Models, Random Walks, Diffusion, Stateful Diffusion
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-231832 (URN)10.1109/ICDCS.2018.00107 (DOI)2-s2.0-85050963074 (Scopus ID)
Conference
38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018; Vienna University of Technology (TU Wien)Vienna; Austria; 2 July 2018 through 5 July 2018
Note

QC 20180703

Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-10-30Bibliographically approved
Bahri, L. & Girdzijauskas, S. (2018). When Trust Saves Energy - A Reference Framework for Proof-of-Trust (PoT) Blockchains. In: WWW '18 Companion Proceedings of the The Web Conference 2018: . Paper presented at The Web Conference 2018 (pp. 1165-1169). ACM Digital Library
Open this publication in new window or tab >>When Trust Saves Energy - A Reference Framework for Proof-of-Trust (PoT) Blockchains
2018 (English)In: WWW '18 Companion Proceedings of the The Web Conference 2018, ACM Digital Library, 2018, p. 1165-1169Conference paper, Published paper (Refereed)
Abstract [en]

Blockchains are attracting the attention of many technical, financial, and industrial parties, as a promising infrastructure for achieving secure peer-to-peer (P2P) transactional systems. At the heart of blockchains is proof-of-work (PoW), a trustless leader election mechanism based on demonstration of computational power. PoW provides blockchain security in trusless P2P environments, but comes at the expense of wasting huge amounts of energy. In this research work, we question this energy expenditure of PoW under blockchain use cases where some form of trust exists between the peers. We propose a Proof-of-Trust (PoT) blockchain where peer trust is valuated in the network based on a trust graph that emerges in a decentralized fashion and that is encoded in and managed by the blockchain itself. This trust is then used as a waiver for the difficulty of PoW; that is, the more trust you prove in the network, the less work you do.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
Keywords
Blockchain, Proof-of-Work, Proof-of-Trust, Trust Graph
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-229925 (URN)10.1145/3184558.3191553 (DOI)978-1-4503-5640-4 (ISBN)
Conference
The Web Conference 2018
Note

QC 20180611

Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2019-04-04Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4516-7317

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