Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTracking bitcoin users activity using community detection on a network of weak signals
90
0
0.0
(
0
)
Added by
Remy Cazabet
Publication date
2017
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Bitcoin is a cryptocurrency attracting a lot of interest both from the general public and researchers. There is an ongoing debate on the question of users anonymity: while the Bitcoin protocol has been designed to ensure that the activity of individual users could not be tracked, some methods have been proposed to partially bypass this limitation. In this article, we show how the Bitcoin transaction network can be studied using complex networks analysis techniques, and in particular how community detection can be efficiently used to re-identify multiple addresses belonging to a same user.
rate research
Read More
Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used. While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success. However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood. Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months. Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity. We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period. This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity. We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app. We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss). However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight). Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC. Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement.
Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoins Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? We present in this paper the first systematic investigation of the degree of decentralization for Bitcoin based on its entire transaction history. We proposed both static and dynamic analysis of Bitcoin transaction network with quantifiable decentralization measures developed based on network analysis and market efficiency study. Case studies are also conducted to demonstrate the effectiveness of our proposed metrics.
Lightning Network (LN) addresses the scalability problem of Bitcoin by leveraging off-chain transactions. Nevertheless, it is not possible to run LN on resource-constrained IoT devices due to its storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LNs functions through a gateway LN node. The idea is to involve the IoT device in LN operations with its digital signature by replacing original 2-of-2 multisignature channels with 3-of-3 multisignature channels. Our protocol enforces the LN gateway to request the IoT devices cryptographic signature for all operations on the channel. We evaluated the proposed protocol by implementing it on a Raspberry Pi for a toll payment scenario and demonstrated its feasibility and security.
We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking.
In this article we identify social communities among gang members in the Hollenbeck policing district in Los Angeles, based on sparse observations of a combination of social interactions and geographic locations of the individuals. This information, coming from LAPD Field Interview cards, is used to construct a similarity graph for the individuals. We use spectral clustering to identify clusters in the graph, corresponding to communities in Hollenbeck, and compare these with the LAPDs knowledge of the individuals gang membership. We discuss different ways of encoding the geosocial information using a graph structure and the influence on the resulting clusterings. Finally we analyze the robustness of this technique with respect to noisy and incomplete data, thereby providing suggestions about the relative importance of quantity versus quality of collected data.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
