Itri icl 0116_distribute
•Download as PPTX, PDF•
2 likes•873 views
1) The document discusses privacy challenges posed by personal data and big data, focusing on data collected from smartphones. 2) It notes that big data analysis is not always beneficial and can result in incorrect or harmful inferences being made about individuals from their data. 3) The document argues that privacy considerations need to be built into the design of platforms and systems from the start to help users maintain control over their data and context.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Itri icl 0116_distribute
Similar to Itri icl 0116_distribute (20)
Itri icl 0116_distribute
- 1. 個資(Personal data)與巨資 (Big Data)的Privacy新挑戰 who cares? - 從手機感測資訊之應用談起 史馥銘 1
- 2. Outline • From Personal Data to Big Data • Demystify Privacy ? • Privacy issues around smartphone sensing • UX for privacy: data and context • Privacy rethink 2
- 3. 你的資料 ≈你 CACM, May 2010 3
- 5. What will you buy next? 5
- 6. What is your risk of getting x? 6
- 7. Why should we care? • Healthy ecosystem for mobile apps, personal data and third parties 7
- 8. Most personal device with rich data 8
- 9. Companies are making inferences from that personal information 9
- 10. Companies are making inferences from that personal information 10
- 11. Companies are making inferences from that personal information 11
- 12. Companies are making inferences from that personal information 12
- 13. Companies are making inferences from that personal information 13
- 14. So What Happened in that Scenario? • Inferences made from both data instances and pattern • Inferences might be incorrect • Data used for one purpose might also be used for another purpose • Inferences might be harmful 14
- 15. Inappropriate Data Use Less trust Less useful data Less monetization 15
- 16. Challenges • What if my data in Big Data is incomplete? • Do I have control to what parts of their data get involved in any Big Data analysis? • What could be the harms? – social groups – insurance – work 16
- 17. Privacy is complicated 17
- 18. Privacy is not security 18
- 19. Privacy is not anonymity 19
- 20. Privacy is not access control list 20
- 21. 問題一 請問美國政府哪一個單位 管Privacy ? FTC (Fair Trade Commission) 公平交易委員會 Why? 21
- 22. Privacy 討論的三大支柱 • Inform and consent • Self-determination (access control) • Personal identifiable information – regulation – de-identification 22
- 23. Inform and consent 還記得你哪一次 點過 Cancel嗎? 23
- 24. 該 Inform 使用者什麼？ 24
- 25. First Step to Improve Privacy on Smartphone • Transparency – What kinds of data are collected by the apps? – Where are they sent to? – How will the data be used? 25
- 27. 先聽結論 Users feel that the apps are intrusive when the apps do not respect data usage contexts 啥?
- 28. Usage Context • 用我的Data OK, 但是(我以為)只能在… • User 心中都有一個*我以為Data這樣用* • Boundary management – Google + – LinkedIn – Facebook 28
- 29. Big Failure of Google Buzz 通訊錄不等於好友! 因為data context 不同 29
- 30. Loss & Collapse of Contexts
- 31. Loss & Collapse of Context reasoning B IF (B ^ C) THEN .. A school drinking C
- 32. Story about my Google Map Google map collects data when the phone is off Moving: sampling rate goes up
- 33. 出乎意料? • Why apps are reading my location when the phone is off?
- 38. E.g. Privacy-impacting Behavior Revealing Privacy-Impacting Behavior Patterns of Smartphone Applications, Gokhan Bal, 2012 38
- 39. 研究議題2: UX for Privacy • 三大支柱之一 ：Self-determination (control) • Do we feel some places are more private then others? • Privacy in public place? • Not dichotomy but involves various factors • Depends on situations
- 40. Research problems • How do we design fine-grained control for people to disclose their data to applications? 40
- 41. Research problems • Could we give more fine-grained control for people to disclose their data to applications? • How do people create a policy? – what are the factors that affect their decisions? 41
- 42. Research problems • Could we give more fine-grained control for people to disclose their data to applications? • How do people create a policy? – what are the factors that affect their decisions? • Control of what? – what should be the appropriate data abstractions for control? – e.g. Google circle, a better abstraction for sharing in social network? 42
- 43. Study Flow • Logs various types of sensors • Prompts the user with a notification 1. annotations (location, situation, timestamp) 2. sensor data (ambient sound, accelerometer, Bluetooth, GPS, Wi-Fi, gyroscope, cellular info..etc ) generate survey Databases 43
- 44. Survey 3 different types of data consumers (apps) (academic, local stores, online companies) are selected randomly for each question) Local store label is customized for each user 44
- 45. Recall context then respond • Without interrupting the user to think about *privacy* questions at the moment, we help the user to reconstruct his situation context later So I was talking to my colleagues this morning in my office, this app asked for my location Yes No [*policy*] I don’t want my locations in work be disclosed to any app 45
- 46. 結論 • We found that context does affect people’s decisions for data disclosure • People actually made non-reasonable decision when they are ignorant of the privacy implications – “I am willing to give my situation to Google because I figure that they might already know everything about me” 46
- 47. 重新思考 今天的 Data 現況 Networked, inferred and public by default 47
- 48. 支配Privacy Practice的三大因素 • Norm (information flow + expectation of privacy) • User trust • Regulation 48
- 49. Where are we heading? • Big Data Analysis + 應用平台 – 電子發票 – Smart City • 如果要把資料效益最大化，應該把 Privacy 納入系統設計 – security/anonymity* – user experience – access control – audit logs 49
- 50. Privacy-Aware Smartphone • Embedded Privacy into OS level • Privacy-impacting factors of smartphone sensing 50
- 51. 個資法 • 科技人請加入對話 51
- 52. 30 seconds takeaway • Big Data is not necessary good data • 隱私 (privacy) 其實不是一個好詞 for issues around personal data in Big Data • Platform designer needs to think about usage of data, not applications • Think context and data together whenever design UX for privacy 52
- 53. 30 seconds takeaway • Big Data is not necessary good data • 隱私 (privacy) 其實不是一個好的形容詞 for issues around personal data in Big Data • Platform designer needs to think about usage of data, not applications • Think context and data together whenever design UX for privacy 53
- 54. 30 seconds takeaway • Big Data is not necessary good data • 隱私 (privacy) 其實不是一個好詞 for issues around personal data in Big Data • Platform designer needs to also think about usage of data and its impact on privacy, not just application features • Think context and data together whenever design UX for privacy 54
- 55. 30 seconds takeaway • Big Data is not necessary good data • 隱私 (privacy) 其實不是一個好詞 for issues around personal data in Big Data • Platform designer needs to also think about usage of data and its impact on privacy, not just application features • Think context and data together whenever design UX for privacy 55
- 56. Appendix 56
Editor's Notes
- Imagine a world where all information about you iscaptured! The places you go, the emails you send, the websites you visit, the people you talk to, the things you do, your tweets, your Facebook updates, your everything. This is the Holy Grail for many people in research. They believe that our lives can be improved by allowing a machine to help us by observing the signals we create. Using these signals, machines can predict the possible actions we will take, and automate the decision making process for us. Machines canmake inferences about you from the data collected about you, and these inferences can be useful in service automation. But these inferences can also be “misleading”. The canonical example is to take someone’s word “out-of-context” which leads to incorrect interpretation of the data. (words you speak).Interpret data in the wrong context raises issues of privacy, and we will talk more about privacy and context in the later slides. The main message of this talk is to ask: Is it possible to preserve the benefits of using personal data for automating decision making, while giving some amount of control to the user?
- 兩個小故事很多人對『你』很有興趣. 但是我們要來看這中間出了什麼問題?0. Facebook1. Target
- one year proposal. a. specify what will I accomplish (沒看見) b. 必須 convince people 我不是要做 situation detection (people 很 confused, people think I am doing situation awareness) b1. 給一個圖表，說明 situation/context-awareness is the input, and what’s the challenges then? c. research challenges: predictions could still have errors (點出如果, situation detection is perfect, what if there are errors?) c1. Usability problem c2. d. plan 如何解決問題2.
- Siri是目前最具代表性的AI程式之一, 一方面說來是AI演算法
- Here is an example of how companies (applications) are making inferences from that personal information. Bob drives to work everyday. He has an iPhone and an application called DriveSmart. Bob lets Drivesmart access his location so that the app will give him the best route every morning. So Bob is happy about that and he drives almost the same route everyday. Drivesmart has now updated to version 2.0, it reads Bob’s driving pattern, and provides some location-based service to recommend stores for breakfast while Bob is on his way to the office.Bob chooses fast food restaurant M, and uses an e-coupon to buy a cup of coffee everyday. Later the week, he gets an email from MASS RMV, entitled “Drive safely, do not eat while driving”. Later he gets another email, from “Eat-healthy-america.org”, saying “Be healthy, Don’t eat fast food for breakfast.”
- Here is an example of how companies (applications) are making inferences from that personal information. Bob drives to work everyday. He has an iPhone and an application called DriveSmart. Bob lets Drivesmart access his location so that the app will give him the best route every morning. So Bob is happy about that and he drives almost the same route everyday. Drivesmart has now updated to version 2.0, it reads Bob’s driving pattern, and provides some location-based service to recommend stores for breakfast while Bob is on his way to the office.Bob chooses fast food restaurant M, and uses an e-coupon to buy a cup of coffee everyday. Later the week, he gets an email from MASS RMV, entitled “Drive safely, do not eat while driving”. Later he gets another email, from “Eat-healthy-america.org”, saying “Be healthy, Don’t eat fast food for breakfast.”
- * We see that Drive smart can calculate a route for Bob giving his current condition and the destination. We also see that the location based service is using Bob’s location pattern to recommend fast food stores on his way to work.** Inferences made about you can be inaccurate, for example, Eat-healthy-america.org made inferences that Bob eats fastfood while drives to work. But in fact, he only gets a cup of coffee from M everyday. ** Data used for one purpose might also be used for another purposeBob’s data is collected by DriveSmart for routing purpose, and later is used for service recommendation and used by RMV****is collected by DriveSmart for routing purpose, and later is used for service recommendation and used by RMV
- boundarysecurityhide secrecyhttps://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcT6R_vuW6B90kTmcoOtuF2QpMpTlaKUOap1P2w8sOfDmzgxOpFW
- 請問…..那Facebook有 privacy issues 嗎? yes, but why ?
- Inform and consentRequire not only the available information to make decision but also having the actual literacy with which to make that decision. What it means about being informed about what’s happening in the algorithm of Big Data? What it means to make about consent: the challenge of agency (knowledge + control Self-determination (access control) Personal Identifiable information
- Pew survey: 54% of people uninstalltheir app because of privacy issues
- 今天雖然我們可以收集很多的資訊，但是有一些資料還不完整，或沒有收集
- 前一項我是外行
- 我現在看到的只有商人, 記者, 律師, 這三種人
- 第一個item需要去找danahboyd的演講
- 第一個item需要去找danahboyd的演講
- 第一個item需要去找danahboyd的演講
- 第一個item需要去找danahboyd的演講privacy is not a good term for describing issues around personal data in big data, then people will ask what are the good terms?