PROVIDENCE: a Flexible Round-by-Round Risk-Limiting Audit
October 17, 2022 Β· Declared Dead Β· π USENIX Security Symposium
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Oliver Broadrick, Poorvi L. Vora, Filip ZagΓ³rski
arXiv ID
2210.08717
Category
cs.CR: Cryptography & Security
Citations
0
Venue
USENIX Security Symposium
Last Checked
4 months ago
Abstract
A Risk-Limiting Audit (RLA) is a statistical election tabulation audit with a rigorous error guarantee. We present ballot polling RLA PROVIDENCE, an audit with the efficiency of MINERVA and flexibility of BRAVO. We prove that PROVIDENCE is risk-limiting in the presence of an adversary who can choose subsequent round sizes given knowledge of previous samples. We describe a measure of audit workload as a function of the number of rounds, precincts touched, and ballots drawn.We quantify the problem of obtaining a misleading audit sample when rounds are too small, demonstrating the importance of the resulting constraint on audit planning. We present simulation results demonstrating the superiority of PROVIDENCE using these measures and describing an approach to planning audit round schedules. We describe the use of PROVIDENCE by the Rhode Island Board of Elections in a tabulation audit of the 2021 election. Our implementation of PROVIDENCE and audit planning tools in the open source R2B2 library should be useful to the states of Georgia and Pennsylvania, which are planning pre-certification ballot polling RLAs for the 2022 general election.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted