printf("text %s\n", formatting, text, here);

• %s (string)
• %d (integer)
• %f (double)

“privacy as an individual right”

• privacy is a control of information: controlling our private information shared with others
  • free choice with alternatives and informed understanding of what’s offered
  • control over personal data collection and aggregation
• privacy as autonomy: your agency to decide for what’s valuable
  • autonomy over our own lives, and our ability to lead them
  • do you have agency?

“privacy as a social group”

• privacy as social good: social life would be severely compromised without privacy
  • privacy allows social
• privacy as a display of trust: privacy enables trusting relationships
  • “fiduciary”: proxy between you and a company
  • “should anyone who has access to personal info have a fiduciary responsibility?”

key trust questions

• who/what do we trust?
• what do we do if trust isn’t upheald?
• how to approach building trust

trust

trust: to stop questioning the responsibility of something

Remember Bayes Rule in Baysian Parameter Learning:

\begin{equation} P\qty(\theta | D) = \frac{P\qty(D | \theta) p \qty(\theta)}{\int_{\theta}P\qty(D | \theta) p \qty(\theta) \dd{\theta}} \end{equation}

we can’t actually easily compute the bottom without taking an analytic integral; instead we can sample from it.

If you want analytical form, you should hope that your likelihood function is a conjugate prior which allows us to analytically update prirors.

fooling a particular circuit as something as random

probability of an event is the proportion of times the event occurs in many repeated trials. It is “our belief that an event \(E\) occurs”.

“the probability of a outcome is a number between 0-1 which highlights how likely the outcome is likely to occur realtive to other outcomes”

Frequentist Definition of Probability

That is, it is a number between \(0-1\). Whereby:

\begin{equation} P(E) = \lim_{n \to \infty} \frac{n(E)}{n} \end{equation}