Jim Snavely ludflu

The evolution of privacy technology

Data sanitizing - supressing identifiers
k-Anonymity (Sweeney & Samarai, 1998) - each individual contained in dataset is indistinguisable from k-1 other users

In practice, it works by a combination of supressing identifiers and bucketing values https://en.wikipedia.org/wiki/K-anonymity The algorithm k-Optimize by Bayardo and Agrawal (2005) approximates k-Anonymity . It aims to perform the "lowest cost" anonymization - meaning it supresses and aggregates data a little as possible in order to achieve the required "k" not great for high-dimensional datasets

Blender Keyboard Shortcuts

Key	Description
n	properties
g	translate (x,y,z)
r	rotate (x,y,z)
s	scale (x,y,z)
e	extrude
i	inset

alerts_by_actions
alerts_by_all
alerts_by_date
alerts_by_departments
alerts_by_employees
alerts_by_encounter
alerts_by_lgl
alerts_by_provider_types
alerts_by_triggers
alerts_with_dis

	import cats.data.State


	object ThingParser {

	def flter[A]( f : A => Boolean ) : State[List[A],List[A]] =
	State(ls => ls.partition(f.andThen(a => !a)))

	def remaining[A] : State[List[A],List[A]] =
	State(ls => (Nil,ls))

	package Journal

	import cats._
	import cats.data.State
	import cats.implicits._
	import com.github.nscala_time.time.Imports._
	import cats.free.Free
	import cats.free.Free.liftF

	import cats.arrow.FunctionK

	--- for sqlfiddle go to http://sqlfiddle.com/#!17

	create table customer(
	id serial primary key,
	name varchar(256)
	);

	create table ServiceOrder(
	id serial primary key,
	description varchar(256),

	Technical questions:

	1. Describe a data pipeline or data warehouse you've built
	2. How do you go about gathering requirements for a data pipeline or warehouse?
	3. How do you unit test ETL systems?
	4. Explain CI/CD for data systems
	5. How do you track data provenance?
	6. What makes a software architecture good or bad? What makes a code module good or bad? A function?
	7. If someone gives you a process that is too slow, how do you improve its performance?
	8. Explain normalized vs denormalized data schemas. Why would you pick one over the other?

	# I wanted to see if my naive find_peaks code would be faster than: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
	# So far the answer is that its about the same. Will run more benchmarks

	def remove_repeats(data):
	acc = []
	head, *tail = data
	acc.append(head)
	for (v,i) in tail:
	(lastv,lasti) = acc[-1]
	if (v != lastv): # only append if element is not the same as last

	{-# OPTIONS_GHC -Wno-missing-methods #-}

	{-# LANGUAGE BlockArguments #-}
	{-# LANGUAGE DeriveAnyClass #-}
	{-# LANGUAGE DeriveGeneric #-}
	{-# LANGUAGE DerivingStrategies #-}
	{-# LANGUAGE RankNTypes #-}

	module Main where

	from llama_index.llms import Ollama
	from llama_index import VectorStoreIndex, SimpleDirectoryReader
	from llama_index import ServiceContext

	from llama_index import (
	ServiceContext,
	SimpleDirectoryReader,
	StorageContext,
	VectorStoreIndex,
	set_global_service_context,