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                            Library & Multimedia
 Modeling Smart Contracts Activities: A Tensor Based Approach
NCJ Number
              306191

Author(s)
          Jeremy Charlier;
                        Radu State;
                        Jean Hilger
Date Published

2019
Length
              7 pages
Annotation
              This paper proposes a new approach for modeling “smart” contracts activities that leverages stochastic processes for series predictions based on the tensor decomposition that can be used for smart contracts predictive analytics.
Abstract
              Smart contracts are autonomous software executing predefined conditions. Two of the biggest advantages of the smart contracts are secured protocols and transaction costs reduction. On the Ethereum platform, an open-source blockchain-based platform, smart contracts implement a distributed virtual machine on the distributed ledger. To avoid denial of service attacks and monetize the services, payment transactions are executed whenever code is being executed between contracts. It is thus natural to investigate if predictive analysis is capable to forecast these interactions. The authors have addressed this issue and propose an innovative application of the tensor decomposition CANDECOMP/PARAFAC to the temporal link prediction of smart contracts. (Published abstract provided)
      
  
          Additional Details
Grant Number(s)
        2009-DE-BX-K219


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Award
        Boston Police Department Predictive Policing Project
    
  
  
Date Published: January 1, 2019
    
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                Denial of service attack
        Contracts



  
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