A Quantitative Study across CIA (Confidentiality, Integrity, Availability) Triad and Performance in Blockchain-Based Crypto-Space

Abstract

This paper presents a new quantitative model to study the interplay across CIA (Confidentiality, Integrity, Availability) Triad and performance in blockchain-based crypto space with specific reference to Ethereum or Ethereum-equivalent chains. The model introduces and incorporates three new random variables on top of the baseline chain model [16], C: the likelihood to secure confidentiality; $\boldsymbol{I}$: to secure integrity; and $\boldsymbol{A}$: to secure availability. Thus, the model will orchestrate an extensive set of key random variables such as λ: transaction slot arrival rate; μ: block posting rate; $\boldsymbol{i}$: number of transaction slots pending on the current block along with $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The underlying mathematical method employed is an embedded Markovian queueing model as the model traces the stochastic flow of the transactions as well as the Markovian flow of them with respect to $\boldsymbol{C}, \boldsymbol{I}$ and $\boldsymbol{A}$. The state in the model is defined by $P_{i_{C / I / A}}$, i.e., the likelihood to have i number of transaction slots pending on the current block and the stochastic CIA status of the crypto space thus far is in C or $\bar{C}, I$ or $\overline{\boldsymbol{I}}$, and $\boldsymbol{A}$ or $\overline{\boldsymbol{A}}$. The solutions to the model will be provided to assess a few basic performance metrics such as W: the average transaction waiting time, $L:$ the average block capacity required; and G: the throughput of transactions per block. And further and primarily, a unique and extensive simulation and analysis will be conducted to evaluate the impact of base random variables such as i, λ, μ, and various combinations of $\mathcal{C}, I$ and A on the overall $P_{i_{C / I / A}}$ in steady state. The results of the simulation reveal tradeoffs between the CIA Triad and performance that is uniquely identifiable by the proposed model.