ProxiLens: Interactive Exploration of High-Dimensional Data using Projections

International audienceAs dimensionality increases, analysts are faced with difficult problems to make sense of their data. In exploratory data analysis, multidimensional scaling projections can help analyst to discover patterns by identifying outliers and enabling visual clustering. However to exploit these projections, artifacts and interpretation issues must be overcome. We present ProxiLens, a semantic lens which helps exploring data interactively. The analyst becomes aware of the artifacts navigating in a continuous way through the 2D projection in order to cluster and analyze data. We demonstrate the applicability of our technique for visual clustering on synthetic and real data sets

Privacy-Preserving Initial Public Offering using SCALE-MAMBA and Hyperledger Fabric

International audienceWe consider Initial Public Offering (IPO) on blockchains while preserving privacy using Secure Multiparty Computation (MPC), which allows participants to perform a computation on secret data. We provide "MPC as a service", where users requiring a computation distributes shares of their data to MPC workers who run an MPC protocol on the shares and return the result. Previous work by Benhamouda et al. considered IPO over Hyperledger Fabric. We improve by providing a tighter and easier integration of MPC protocol in Fabric using the MPC library SCALE-MAMBA. We explain the obtained security benefits and experimental results are provided

Etude des projections de données comme support interactif de l'analyse visuelle de la structure de données de grande dimension

The cost of data acquisition and processing has radically decreased in both material and time. But we also need to analyze and interpret the large amounts of complex data that are stored. Dimensionality is one aspect of their intrinsic complexity. Visualization is essential during the analysis process to help interpreting and understanding these data. Projection represents data as a 2D scatterplot, regardless the amount of dimensions. However, this visualization technique suffers from artifacts due to the dimensionality reduction. Its lack of reliability implies issues of interpretation and trust. Few studies have been devoted to the consideration of the impact of these artifacts, and especially to give feedbacks on how non-expert users can visually analyze projections. The main approach of this thesis relies on an taking these artifacts into account using interactive techniques, in order to allow data scientists or non-expert users to perform a trustworthy visual analysis of projections. The interactive visualization of the proximities applies a coloring of the original proximities relatives to a reference in the data-space. This interactive technique allows revealing projection artifacts in order to help grasping details of the underlying data-structure. In this thesis, we redesign this technique and we demonstrate its potential by presenting two controlled experiments studying the impact of artifacts on the visual analysis of projections. We also present a design-space based on the lens metaphor, in order to improve this technique and to locally visualize a projection free of artifacts issues.Acquérir et traiter des données est de moins en moins coûteux, à la fois en matériel et en temps, mais encore faut-il pouvoir les analyser et les interpréter malgré leur complexité. La dimensionnalité est un des aspects de cette complexité intrinsèque. Pour aider à interpréter et à appréhender ces données le recours à la visualisation est indispensable au cours du processus d’analyse. La projection représente les données sous forme d’un nuage de points 2D, indépendamment du nombre de dimensions. Cependant cette technique de visualisation souffre de distorsions dues à la réduction de dimension, ce qui pose des problèmes d’interprétation et de confiance. Peu d’études ont été consacrées à la considération de l’impact de ces artefacts, ainsi qu’à la façon dont des utilisateurs non-familiers de ces techniques peuvent analyser visuellement une projection. L’approche soutenue dans cette thèse repose sur la prise en compte interactive des artefacts, afin de permettre à des analystes de données ou des non-experts de réaliser de manière fiable les tâches d’analyse visuelle des projections. La visualisation interactive des proximités colore la projection en fonction des proximités d’origine par rapport à une donnée de référence dans l’espace des données. Cette technique permet interactivement de révéler les artefacts de projection pour aider à appréhender les détails de la structure sous-jacente aux données. Dans cette thèse, nous revisitons la conception de cette technique et présentons ses apports au travers de deux expérimentations contrôlées qui étudient l’impact des artefacts sur l’analyse visuelle des projections. Nous présentons également une étude de l’espace de conception d’une technique basée sur la métaphore de lentille et visant à s’affranchir localement des problématiques d’artefacts de projection

Study of multidimensional scaling as an interactive visualization to help the visual analysis of high dimensional data

Acquérir et traiter des données est de moins en moins coûteux, à la fois en matériel et en temps, mais encore faut-il pouvoir les analyser et les interpréter malgré leur complexité. La dimensionnalité est un des aspects de cette complexité intrinsèque. Pour aider à interpréter et à appréhender ces données le recours à la visualisation est indispensable au cours du processus d’analyse. La projection représente les données sous forme d’un nuage de points 2D, indépendamment du nombre de dimensions. Cependant cette technique de visualisation souffre de distorsions dues à la réduction de dimension, ce qui pose des problèmes d’interprétation et de confiance. Peu d’études ont été consacrées à la considération de l’impact de ces artefacts, ainsi qu’à la façon dont des utilisateurs non-familiers de ces techniques peuvent analyser visuellement une projection. L’approche soutenue dans cette thèse repose sur la prise en compte interactive des artefacts, afin de permettre à des analystes de données ou des non-experts de réaliser de manière fiable les tâches d’analyse visuelle des projections. La visualisation interactive des proximités colore la projection en fonction des proximités d’origine par rapport à une donnée de référence dans l’espace des données. Cette technique permet interactivement de révéler les artefacts de projection pour aider à appréhender les détails de la structure sous-jacente aux données. Dans cette thèse, nous revisitons la conception de cette technique et présentons ses apports au travers de deux expérimentations contrôlées qui étudient l’impact des artefacts sur l’analyse visuelle des projections. Nous présentons également une étude de l’espace de conception d’une technique basée sur la métaphore de lentille et visant à s’affranchir localement des problématiques d’artefacts de projection.The cost of data acquisition and processing has radically decreased in both material and time. But we also need to analyze and interpret the large amounts of complex data that are stored. Dimensionality is one aspect of their intrinsic complexity. Visualization is essential during the analysis process to help interpreting and understanding these data. Projection represents data as a 2D scatterplot, regardless the amount of dimensions. However, this visualization technique suffers from artifacts due to the dimensionality reduction. Its lack of reliability implies issues of interpretation and trust. Few studies have been devoted to the consideration of the impact of these artifacts, and especially to give feedbacks on how non-expert users can visually analyze projections. The main approach of this thesis relies on an taking these artifacts into account using interactive techniques, in order to allow data scientists or non-expert users to perform a trustworthy visual analysis of projections. The interactive visualization of the proximities applies a coloring of the original proximities relatives to a reference in the data-space. This interactive technique allows revealing projection artifacts in order to help grasping details of the underlying data-structure. In this thesis, we redesign this technique and we demonstrate its potential by presenting two controlled experiments studying the impact of artifacts on the visual analysis of projections. We also present a design-space based on the lens metaphor, in order to improve this technique and to locally visualize a projection free of artifacts issues

An Empirical Analysis of Pool Hopping Behavior in the Bitcoin Blockchain

International audienceWe provide an empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Mining pools have emerged as major players to ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that payout schemes and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale

MiningVis: Visual Analytics of the Bitcoin Mining Economy

International audienceWe present a visual analytics tool, MiningVis, to explore the long-term historical evolution and dynamics of the Bitcoin mining ecosystem. Bitcoin is a cryptocurrency that attracts much attention but remains difficult to understand. Particularly important to the success, stability, and security of Bitcoin is a component of the system called "mining.'' Miners are responsible for validating transactions and are incentivized to participate by the promise of a monetary reward. Mining pools have emerged as collectives of miners that ensure a more stable and predictable income. MiningVis aims to help analysts understand the evolution and dynamics of the Bitcoin mining ecosystem, including mining market statistics, multi-measure mining pool rankings, and pool hopping behavior. Each of these features can be compared to external data concerning pool characteristics and Bitcoin news. In order to assess the value of MiningVis, we conducted online interviews and insight-based user studies with Bitcoin miners. We describe research questions tackled and insights made by our participants and illustrate practical implications for visual analytics systems for Bitcoin mining

The evolution of mining pools and miners’ behaviors in the Bitcoin blockchain

International audienceWe analyzed 23 mining pools and explore the mobility of miners throughout Bitcoin's history. Mining pools have emerged as major players to ensure that the Bitcoin system stays secure, valid, and stable. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave the pools. We investigated the reward payout flow of mining pools and characterized them based on payout irregularity and structural complexity. Based on our proposed algorithm, we identified miners and studied their mobility in the pools over time. Our analysis shows that Bitcoin mining is an industry that is sensitive to external events (e.g., market price and government policy). Over time, competition between pools involving reward schemes and pool fees motivated miners to migrate between pools (i.e., pool hopping and cross pooling). These factors converged toward optimal scheme and values, which made mining activities more stable

Interactive Demo: Visualization for Bitcoin Mining Pools Analysis

International audienceWe demonstrate an interactive visualization tool to analyze Bitcoin mining pools. The tool allows analysts to see the evolution of mining pools distribution over time and relationships with external variables, i.e., Bitcoin statistics and news headlines. Moreover, we also display information about pool hopping among mining pools to help understand the internal dynamics of miners

Towards Anonymous, Unlinkable, and Confidential Transactions in Blockchain

International audience—In this paper, we investigate the issues of data and users' privacy in decentralized environments. We propose a novel security and privacy-preserving protocol for the blockchain that addresses the limitations of existing approaches, mainly the anonymity and unlinkability of users' identities and the privacy of transactions. We highlight the benefits of our proposed protocols across various use cases and we theoretically analyze its efficiency and robustness