Integrate: answer to question "can observations be made public" into preprocessors and release text

[raprasad]

• [ ] 1. create a checklist table of current stats and if/how the computation changes when the # of observations cannot be made public

See google doc: https://docs.google.com/document/d/1xUihcjh4zmfnhG0-2EC-uG-qzpde8WXphRksB0NvHe8/edit#

(Redo steps below after doc discussion)

• [ ] ~~2. update the StatSpec class (stat_spec.py) to include a variable indicating is_dataset_size_public~~
• [ ] ~~3. ^ update the computation chains for existing stats appropriately.~~
  • [ ] ~~e.g. if the is_dataset_size_public == True, update the chain, use a different chain, etc.~~
  • [ ] ~~include tests for each stat. (Check taht if the dataset size is private then more epsilon is used, etc.~~
• [ ] ~~4. Integrate into larger workflow. e.g. ValidateReleaseUtil.build_stat_specs()~~
  • ~~ValidateReleaseUtil.__init__ : add self.is_dataset_size_public = None~~
  • ~~ValidateReleaseUtil.run_preliminary_steps: set self.is_dataset_size_public to True or False~~
  • ~~Add function DatasetInfo.is_dataset_size_public()~~
    • ~~similar to get_dataset_size()~~
    • ~~except finds answer to the dataset question within DepositorSetupInfo~~
  • ~~ValidateReleaseUtil.build_stat_specs(), user self.is_dataset_size_public when building the StatSpec objects~~