Accuracy and Privacy

I will post regular updates about data publication plans for the 2020 Census. I won't be shy about statistics, include some history and, ultimately, address the implications of technical decisions on politics, planning, research... and journalism.

The hottest Substack posts of Accuracy and Privacy

And their main takeaways

Differential privacy, an easy case

1 HN point • 02 Jan 19
🕹 Technology Privacy Statistics Data Collection Data processing
  1. Differential privacy is a mathematical definition of privacy specifically designed for protecting personal data in a world of big data and computation.
  2. Privacy protection in differential privacy comes from adding randomness or noise to data before publishing, where more noise equals greater privacy protection.
  3. There is a tradeoff between accuracy and privacy in differential privacy, as the level of uncertainty introduced for privacy protection can impact the accuracy of conclusions drawn from the data.

Accuracy and Privacy

0 implied HN points • 20 Dec 18
🇺🇸 U.S. Politics Data Privacy Legislation Legal issues
  1. Decisions are currently being made about how Census data will be published in 2020 with a focus on protecting respondent confidentiality through a new "formal privacy" framework.
  2. The Census Bureau is required to keep data private and is not allowed to release individual identifying information, but there are concerns about the effectiveness of current disclosure limitation procedures in today's data ecosystem.
  3. There is an ongoing debate about balancing the mathematical guarantees of a formal privacy mechanism with the concerns of end users of Census data, which may potentially lead to legal challenges.