FTCಪರಿಶೀಲಿಸಿದ ಖಾತೆ

@FTC

Official tweets from FTC. Twitter’s privacy policy governs here. Info on chats, privacy: Retweets ≠ Endorsements

Washington, D.C.
ಸೆಪ್ಟೆಂಬರ್ 2010 ಸಮಯದಲ್ಲಿ ಸೇರಿದ್ದಾರೆ

@FTC ತಡೆಹಿಡಿಯಲಾಗಿದೆ

ನೀವು ಖಚಿತವಾಗಿಯೂ ಈ ಟ್ವೀಟ್‌ಗಳನ್ನು ನೋಡಲು ಬಯಸುವಿರಾ? ಟ್ವೀಟ್‌ಗಳನ್ನು ನೋಡುವುದು @FTC ಅವರನ್ನು ತಡೆತೆರವುಗೊಳಿಸುವುದಿಲ್ಲ.

  1. Wang: Trackers should clearly explain data they collect.

  2. Wang: Implications for privacy design/policy: tools can't assume users know about 3rd parties.

  3. Wang discussing different ways or models of how think about targeted ads.

  4. Wang: Why folk models matter -- understand user attitudes towards OBA, customize user education, influence user behavior.

  5. Yang Wang (SALT Lab, Syracuse University): People have mixed feelings about Online Behavioral Advertising (OBA)

  6. Phelan: Practical policy implication - focus on considered concern. Elicit only considered concern, encourage congruence.

  7. Phelan: Privacy paradox -- people say they care about privacy but still give away their data.

  8. Chanda Phelan (Univ. of Michigan): Interviews found that people look at social presence, low marginal risk and trust

  9. "Data collection contexts affect how users value their friends' info. Users will trade off friends' privacy 4 app performance"

  10. Jens G. & Yu Pu: App usrs are "privacy egoists" and privacy knowledge impacts interdependent privacy valuations

  11. Jens Grossklags (Penn State & Technical Univ. of Munich) & Yu Pu (Penn State) explain factors driving concern towrds own privacy

  12. We’re also joined by Chanda Phelan (Univ. of Michigan), Yang Wang (Syracuse Univ.) & Mahmood Sharif (Carnegie Mellon Univ.)

  13. We’re joined by Jens Grossklags (Penn State & Technical Univ. of Munich) & Yu Pu (Penn State)

  14. For our next session, we’ll explore computer privacy expectations moderated by FTC’s Chief Technologist,

  15. Statement of FTC Chairwoman Edith Ramirez following adoption of the Swiss-U.S. Privacy Shield Framework:

  16. . Q: What role should privacy policies have? Zimmeck: Prob oriented to lawyers, could tell users when difference.

  17. Wijesekera: If u crowdsource privacy decision making,might miss the contextual cues used to make decisions. More research needed

  18. Q: Could automated privacy decision making be subject to bias? Wijesekera: Decisions made on runtime info, not user gender, etc.

  19. From audience Q, : Considered ethical implications of tool & made sure processing on device and no identifiers sent back.

  20. Please tweet questions using & & we’ll get back to you! If we can’t get to u 2day, staff will follow up!

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