Authors
Man-Ki Yoon
Zhong Shao
Abstract
Reasoning about the decision-making process of modern autonomous
systems becomes increasingly challenging as their software systems
become more inexplicable due to complex data-driven processes. Yet,
logs of data production and consumption among the software components
can provide useful run-time evidence to analyze and diagnose faulty
operations. Particularly when the system is run by a number of
software components that were individually developed by different
parties (e.g., open source, third-party vendor), it is imperative to
find out where the problems originated and thus who should be
responsible for the problems. However, software components may act
unfaithfully or non-cooperatively to make the run-time evidence
refutable or unusable. Hence, this paper presents Accountable Data
Logging Protocol (ADLP), a mechanism to build accountability into data
distribution among software components that are not necessarily
cooperative or faithful in reporting the logs of their data production
and consumption. We demonstrate an application of ADLP to a
miniaturized self-driving car and show that it can be used in practice
at a moderate performance cost.
Published
In
Proc. 2019 IEEE 39th International Conference on
Distributed Computing Systems (ICDCS'19),
Dallas, Texas. Pages 1149-1160, July 2019.