Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Voting by mail: a Markov chain model for managing the security risks of election systems

Carmen A. Haseltine, Laura A. Albert

Abstract

The scrutiny surrounding vote-by-mail (VBM) in the United States has increased in recent years, highlighting the need for a rigorous quantitative framework to evaluate the resilience of the absentee voting infrastructure. This paper addresses these issues by introducing a dynamic mathematical modeling framework for performing a risk assessment of VBM processes. We introduce a discrete-time Markov chain (DTMC) to model the VBM process and assess election performance and risk with a novel layered network approach that considers the interplay between VBM processes, malicious and non-malicious threats, and security mitigations. The time-inhomogeneous DTMC framework captures dynamic risks and evaluates performance over time. The DTMC model accounts for a spectrum of outcomes, from unintended voter errors to sophisticated, targeted attacks, representing a significant advancement in the risk assessment of VBM planning and protection. A case study based on real-world data from Milwaukee County, Wisconsin, is used to evaluate the DTMC model. The analysis includes hypothetical worst-case attack scenarios to stress-test VBM processes and to assess the efficacy of security measures and the impact of different attack timings. The analysis suggests that ballot drop boxes and automatic ballot notification systems are crucial for reducing the attack surface to ensure secure and reliable operations.

Voting by mail: a Markov chain model for managing the security risks of election systems

Abstract

The scrutiny surrounding vote-by-mail (VBM) in the United States has increased in recent years, highlighting the need for a rigorous quantitative framework to evaluate the resilience of the absentee voting infrastructure. This paper addresses these issues by introducing a dynamic mathematical modeling framework for performing a risk assessment of VBM processes. We introduce a discrete-time Markov chain (DTMC) to model the VBM process and assess election performance and risk with a novel layered network approach that considers the interplay between VBM processes, malicious and non-malicious threats, and security mitigations. The time-inhomogeneous DTMC framework captures dynamic risks and evaluates performance over time. The DTMC model accounts for a spectrum of outcomes, from unintended voter errors to sophisticated, targeted attacks, representing a significant advancement in the risk assessment of VBM planning and protection. A case study based on real-world data from Milwaukee County, Wisconsin, is used to evaluate the DTMC model. The analysis includes hypothetical worst-case attack scenarios to stress-test VBM processes and to assess the efficacy of security measures and the impact of different attack timings. The analysis suggests that ballot drop boxes and automatic ballot notification systems are crucial for reducing the attack surface to ensure secure and reliable operations.

Paper Structure

This paper contains 23 sections, 2 equations, 6 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Markov Chain Framework
  4. Process Layer
  5. Final Ballot States
  6. Attacks Layer
  7. Mitigations Layer
  8. Modeling Assumptions
  9. DTMC Model
  10. Wisconsin 2020 General Election Case Study
  11. Time
  12. Transitions
  13. Calibration
  14. Validation
  15. Computational Results
  16. ...and 8 more sections

Figures (6)

  • Figure 1: Example of a portion of the VBM attack tree EAC_2009
  • Figure 2: Layered Network for time intervals $1 \le t < T-1$
  • Figure 3: Layered Network for VBM on Election Day at time interval $t\ge T-1$
  • Figure 4: Comparison of recorded and modeled returned ballots for the state of Wisconsin for the 2020 General Election. The graph shows daily ballots returned in blue and the DTMC model baseline for daily ballots returned as a dashed line.
  • Figure 5: Deviation in counted and unaltered (C, U) ballots under three moderate malicious attack scenarios ($X9$, $X13$, and $X29$) launched on different days.
  • ...and 1 more figures