Tweak slides.

website/docs/resources/slides/lecture-welcome.md

@@ -14,10 +14,6 @@ date: September 04, 2019
 
 ![](images/broken.png)
 
-## It's really difficult!
-
-![](images/netflix.png)
-
 # What topics to cover?
 
 ## A really, really vast field
@@ -173,7 +169,7 @@ date: September 04, 2019
 ## Todos for you
 0. Complete the [course survey](https://forms.gle/NvYx3BM7HVkuzYdG6)
 1. Explore the [course website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/)
-2. Think about which lecture you want to present and summarize
+2. Think about which lecture you want to present
 3. Think about which lecture you want to summarize
 4. Form project groups and brainstorm topics
 
@@ -217,10 +213,28 @@ date: September 04, 2019
 - Really, really hard to think about side information
     - May not even be public at time of data release!
 
-## Netflix challenge
+## Netflix prize
 - Database of movie ratings
 - Published: ID number, movie rating, and rating date
-- Attack: from public IMDB ratings, recover names for Netflix data
+- Competition: predict which movies IDs will like
+- Result
+    - Tons of teams competed
+    - Winner: beat Netflix's best by **10%**
+
+> A triumph for machine learning contests!
+
+## 
+
+![](images/netflix.png)
+
+
+## Privacy flaw?
+- Attack
+    - Public info on IMDB: names, ratings, dates
+    - Reconstruct names for Netflix IDs 
+- Result
+    - Netflix settled lawsuit ($10 million)
+    - Netflix canceled future challenges
 
 ## "Blending in a crowd"
 - Only release records that are similar to others
@@ -233,14 +247,17 @@ date: September 04, 2019
     - First few queries fine, then suddenly total violation
 - Again, interacts poorly with side-information
 
-## Differential privacy
-- Proposed by Dwork, McSherry, Nissim, Smith (2006)
+# Differential privacy
+
+## Yet another privacy definition
 
 > A new approach to formulating privacy goals: the risk to one’s privacy, or in
 > general, any type of risk... should not substantially increase as a result of
 > participating in a statistical database.  This is captured by differential
 > privacy.
 
+- Proposed by Dwork, McSherry, Nissim, Smith (2006)
+
 ## Basic setting
 - Private data: set of records from individuals
     - Each individual: one record
@@ -256,3 +273,10 @@ subset $S$ of outputs, we have:
 $$
 \Pr[ Q(db) \in S ] \leq e^\varepsilon \cdot \Pr[ Q(db') \in S ] + \delta
 $$
+
+## Basic reading
+> Output of program doesn't depend too much on any single person's data
+
+- Property of the algorithm/query/program
+    - No: "this data is differentially private"
+    - Yes: "this query is differentially private"