I need someone to help me study the study guide

DS300​ ​-​ ​FALL​ ​2017 Dr.​ ​Squicciarini

Study​ ​Guide The​ ​exam​ ​is​ ​in​ ​class​ ​on​ ​Sept​ ​21st​ ​-​ ​it​ ​should​ ​take​ ​max​ ​45​ ​minutes.​ ​​ ​We​ ​will​ ​discuss​ ​in class​ ​how​ ​to​ ​use​ ​the​ ​study​ ​guide. For​ ​every​ ​learning​ ​module,​ ​there​ ​is​ ​a​ ​list​ ​of​ ​main​ ​topics​ ​discussed​ ​in​ ​class​ ​and​ ​related readings.​ ​Case​ ​studies​ ​and​ ​examples​ ​need​ ​not​ ​to​ ​be​ ​known​ ​in​ ​depth​ ​but​ ​understood​ ​in the​ ​context​ ​of​ ​one​ ​or​ ​more​ ​of​ ​the​ ​listed​ ​topics. The​ ​exam​ ​questions​ ​will​ ​mainly​ ​be​ ​based​ ​on​ ​material​ ​from​ ​lectures​ ​(and​ ​also​ ​what​ ​we discussed​ ​in​ ​the​ ​readings);​ ​some​ ​questions​ ​will​ ​be​ ​based​ ​on​ ​material​ ​that​ ​is​ ​only covered​ ​in​ ​the​ ​reading​ ​material,​ ​and​ ​others​ ​will​ ​only​ ​come​ ​from​ ​lecture.​ ​​ ​Remember, you​ ​won’t​ ​be​ ​asked​ ​about​ ​simple​ ​definitions​ ​or​ ​facts​ ​–​ ​so​ ​make​ ​sure​ ​you​ ​understand​ ​the following​ ​concepts.

L01:​ ​Introduction​ ​and​ ​Social​ ​Aspects​ ​of​ ​Privacy​ ​(Aug​ ​22-Sept​ ​1) Main​ ​Topics

1. Data​ ​science​ ​and​ ​Privacy​ ​Concerns 2. Overview​ ​of​ ​Ethical​ ​Concerns 3. Privacy

a. Definition​ ​and​ ​main​ ​role​ ​in​ ​the​ ​context​ ​of​ ​workflow​ ​applications 4. Examples​ ​of​ ​applications​ ​and​ ​domains​ ​where​ ​privacy​ ​is​ ​a​ ​main​ ​challenge 5. Privacy​ ​Violations

a. Identify​ ​the​ ​possible​ ​violations​ ​by​ ​type b. Be​ ​able​ ​to​ ​briefly​ ​discuss​ ​them

Materials ● Read​ ​​Chapter​ ​1​​ ​of​ ​Humanizing​ ​big​ ​Data​ ​book​ ​from​ ​Strong,​ ​C.

○ You​ ​may​ ​skip​ ​the​ ​section​ ​labeled​ ​“This​ ​Book” ● Review​ ​​Slides​ ​Aug​ ​29th

L02:​ ​Data​ ​Science​ ​and​ ​Privacy​ ​Legal,​ ​Social​ ​and​ ​Ethical​ ​Aspects​ ​of​ ​Privacy​ ​(end​ ​on​ ​Sept 21) PART​ ​1 Main​ ​Topics

1. Privacy​ ​challenges​ ​​ ​during​ ​Data​ ​Collection​ ​-​ ​end​ ​user​ ​perspective a. Privacy​ ​policies​ ​in​ ​online​ ​contexts,​ ​unwanted​ ​or​ ​unexpected​ ​consequences​ ​etc.

2. Privacy​ ​Economics​ ​(endowment​ ​effect,​ ​privacy​ ​paradox​ ​etc) a. Provide​ ​definition​ ​and​ ​examples​ ​of​ ​each​ ​effect

3. Ethical​ ​Concerns​ ​in​ ​Data​ ​Science a. Be​ ​able​ ​to​ ​define​ ​ethical​ ​issues​ ​and​ ​provide​ ​example​ ​example​ ​applications​ ​where

such​ ​issues​ ​arise​ ​(e.g.​ ​case​ ​studies​ ​seen​ ​at​ ​class) 4. Privacy​ ​Laws​ ​and​ ​Regulations​ ​(​as​ ​discussed​ ​at​ ​class​)

a. FIPS​ ​principles:​ ​​Know​ ​them​ ​and​ ​why​ ​they​ ​are​ ​important b. European​ ​vs​ ​US​ ​Laws:​ ​​Discuss​ ​main​ ​differences

Materials

● Lotus​ ​Case​ ​Study​ ​Discussion ● OkCupid​ ​Study​ ​Reveals​ ​the​ ​Perils​ ​of​ ​Big-Data​ ​Science​ ​[​WIRED​] ● Documentary:​ ​​“Terms​ ​and​ ​Conditions​ ​May​ ​Apply"​​ ​(discussed​ ​in​ ​Class​ ​on

Tuesday​ ​Sept​ ​5) ● Read​ ​​Chapter​ ​13​ ​"Privacy​ ​Paradox"​​ ​of​ ​the​ ​book​ ​from​ ​Strong,​ ​C.​ ​"Humanizing​ ​Big

Data.” ● Review​ ​​Slides​ ​Sept​ ​5th ● Read​ ​​I​ ​Didn't​ ​Buy​ ​It​ ​For​ ​Myself​​ ​by​ ​Lorrie​ ​Cranor.​ ​You​ ​may​ ​skip​ ​Section​ ​5.2​ ​and

Section​ ​5.3

PART​ ​2 Main​ ​Topics

1. Quasi​ ​Identifiers,​ ​Sensitive​ ​Data,​ ​NonSensitive​ ​Data,​ ​Explicit​ ​Identifiers​ ​- definitions,​ ​differences​ ​(e.g.​ ​contextual​ ​differences)​ ​and​ ​examples

2. Privacy​ ​vs.​ ​Anonymity​ ​​(be​ ​able​ ​to​ ​distinguish​ ​and​ ​discuss​ ​examples​ ​of​ ​each​) 3. Anonymity-​ ​Masking,​ ​De-identification​ ​(​definition​ ​and​ ​examples​ ​showing​ ​how​ ​to​ ​apply

these​ ​methods​) 4. Privacy​ ​Vs​ ​Utility​ ​(​Challenges​ ​and​ ​definition​ ​of​ ​both​ ​in​ ​the​ ​context​ ​of​ ​transactional​ ​data​) 5. Challenges​ ​with​ ​protecting​ ​multiple​ ​types​ ​of​ ​data​ ​(​Multidimensional​ ​data) 6. Examples​ ​of​ ​de-identification​ ​issues 7. Sensitive​ ​data​ ​in​ ​databases​ ​​(know​ ​factors​ ​that​ ​make​ ​data​ ​sensitive​ ​and​ ​types​ ​of

disclosure) 8. Inference​ ​in​ ​databases

Materials

● Read​ ​​Chapter​ ​1​​ ​​from​ ​"Data​ ​Privacy"​ ​N.​ ​Venkataramanan​ ​and​ ​A.​ ​Shriram. ○ You​ ​may​ ​skip​ ​sections​ ​1.1​ ​and​ ​1.3.2.

● Read​ ​​"​But​ ​the​ ​data​ ​is​ ​already​ ​public​”​ ​article. ● Read​ ​​Section​ ​6.4​ ​and​ ​Section​ ​6.5​ ​of​ ​Pflegger​ ​book ● Review​ ​​Slides​ ​Sept.​ ​12th ● Review​​ ​​Slides​ ​Sept​ ​13th