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Mobile privacysurvey presentation

Jiang Zhu
Sr. Staff Engineer/Sr. Research Scientist um Cisco Systems
21. Aug 2012
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Mobile privacysurvey presentation

  1. Nitisha Desai, Sean Wang and Jiang Zhu November 23rd, 2011 1
  2. • Privacy in the news • TaintDroid 2
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  4. • Addresses of websites • Share with other companies •URLS • Verizon will use this information for •Search Terms •Business & Marketing Reports • Location Details •Making relevant mobile ads • App and Device usage • Use of Verizon Products • Demographic categories •Gender •Age •Sports •Frequent Diner 4
  5. • “I know where you were and what you are Sharing: Exploiting P2P Communications to Invade Users‟ Privacy” • An attacker can Identify a person, their location and filesharing habits 5
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  7. • Collected children‟s • “Unsubtantiated • P2P File Sharing personal and deceptive” exposed app users‟ information without personal parental consent information without authorization • Violated COPPA 7
  8. • Geolocational Privacy and Surveillance Act • Creates rules to govern the interception and disclosure of geolocation information • Prohibits unlawfully intercepted geolocation information to be used as evidence 8
  9. • Require companies to tell users when location data is being collected • Allow the users to decide whether or not to disclose this information to third parties 9
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  11. • “With more than 58% of U.S. mobile users worried that their data can be easily accessed by others, a privacy policy that helps establish and maintain consumer trust is absolutely essential.” • Create a framework for developers to use to provide clear and functional privacy disclosures to consumers who use mobile applications. 11
  12. Policy maker Policy Language Code Guidance Resources 12
  13. Authors: William Enck, Peter Gilbert, Byung-Gon Chun, Landon P.Cox, Jaeyeon Jung, Patrick McDaniel and Anmo N.Sheth. Slide credits: William Enck, Steven Zittrower 13
  14. • What is TaintDroid • Why it‟s Important • Implementation • Costs and Tradeoffs • Results 14
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  16. GPS/Location Data Camera/Photos/Microphone Contacts SMS Messages SIM Identifiers (IMSI, ICC-ID, IMEI)
  17. • Goals: Monitor app behavior to determine when privacy sensitive information leaves the phone • Challenges .. • Smartphones are resource constrained • Third-party applications are entrusted with several types of privacy sensitive information • Context-based privacy information is dynamic and can be difficult to identify even when sent in the clear • Applications can share information 18
  18. Dynamic Taint Analysis • Dynamic taint analysis is ais a technique that tracks 1. Dynamic taint analysis technique that tracks the information information dependencies from an origin dependencies from it origin. • Conceptual idea: 2. Conceptual Ideas: c = t ai nt _sour ce( ) ‣ a. Taint source Taint source ... ‣ b. Taint propagation Taint propagation c. Taint sink a = b + c ‣ Taint sink ... net wor k_send( a) • Limitations: performance and granularity is a trade-off ystems and Internet Infrastructure Security Laboratory (SIIS) Page 5 19
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  20. TaintDroid Architecture map courtesy of TaintDroid: An Information-Flow… Interpreted Code Trusted Applications Untrusted Applications 8 Trusted Library Taint Source 1 Taint Sink 2 3 6 7 9 Taint Map Taint Map Userspace Dalvik VM Dalvik VM Interpreter Interpreter 4 Binder IPC Library Binder IPC Library Binder Hook Binder Hook 5 Kernel Binder Kernel Module 21
  21. ‣ Patches state after native method invocation ‣ Extends tracking between applications and to storage Message-level tracking Alci n o pi a Ce p to d M sg Alci n o pi a Ce p to d Va it l ru Va it l ru Variable-level Mie an ch Mie an ch tracking Method-level NvSt m rr s a eye L a t i s i i b e tracking File-level N o Itr c e r nf e t k e w a So a S a e n r t rg c dy o e tracking • Variables Local variables, arguments, class static fields, class instances, and arrays • TaintDroid is a firmware modification, not an app • Messages ystems and Internet Infrastructure Security Laboratory (SIIS) Page 6 Taint tag is upper bound of tainted variables in message • Methods Tracks and propagates system provided native libraries • Files One tag per-file, same logic as messages 22
  22. Sources Sinks • Low-bandwidth • Network Calls Sensors • File-system Writes • High-bandwidth Sensors • Information Databases • Devices Identifiers 23
  23. • The authors modified the Dalvik VM interpreter to store and propagate taint tags (a taint bit-vector) on variables. • Local variables and tags: taint tags stored adjacent to variables on the internal execution stack. -- 32-bit bitvector with each variable 24
  24. • Rules for passing taint markers • α←C : τα←0 • β←α:τβ←τα • α„←α⊗β:τα←τα∪τβ • … • Govern steps 3, 7 of TaintDroid Architecture 25
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  27. • 14% overall overhead. Smallest for arithmetic and logic operations; greatest for string operations • 4.4% memory overhead 28
  28. 25 21.88 MB 21.06 MB 19.48 MB 18.92 MB 20 15 10.89 ms Android 8.58 ms TaintDroid 10 5 0 App Load Time Address Book © Address Book ® 27% slower 3.5% more memory 29
  29. 30% 25% 20% 15% 10% 5% 0% App Load Addres Book Addres Book Phone Call Take Picture Time (create) (read) 63:65 348:367 101:119 96:106 1718:2216 (Android: TaintDriod in ms) 30
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  31. • Selected 30 applications with bias on popularity and access to Internet, location, microphone, and camera • 100 minutes, 22,594 packets, 1,130 TCP connections • Of 105 flagged TCP connections, only 37 legitimate. 32
  32. • 15 of the 30 applications shared physical location with an ad server (admob.com, ad.qwapi.com, ads.mobclix.com, data.flurry.com) • Most traffic was plaintext (e.g., AdMob HTTP GET): • In no case was sharing obvious to user or in EULA • In some cases, periodic and occurred without app use 33
  33. • 7 applications sent device (IMEI) and 2 apps sent phone information (Phone #, IMSI*, ICC-ID) to a remote server without informing the user. One app‟s EULA indicated the IMEI was sent Another app sent the hash of the IMEI • Frequency was app-specific, e.g., one app sent phone information every time the phone booted. • Appeared to be sent to app developers ... 34
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  35. • Approach Limitations • TaintDroid only tracks data flows (i.e. explicit flows). • Malicious application can game out TaintDroid and exflitrate privacy sensitive information through control flow. • Taint Source Limitations • IMSI contains country (MCC), network (MNC) and Station (MSIN) codes. All tainted together, but heavily used in Android for configuration parameters. Likely to cause false positives. • Network only as sink . Sensitive information can propagate back from network. • Requires custom OS modification. No checks on native libraries • Lack of evaluation data on power consumption • User Interface: log is too technical and need further inspection 37
  36. • TaintDroid provides efficient, system-wide, dynamic taint tracking and analysis for Android • 4 granularities of taint propagations • Variable-level • Message-level • Method-level • File-level • 14% performance overhead on a CPU-bound microbenchmark. • Identified 20 out of the 30 random selected applications to share information in a way that was not expected. • Findings demonstrated the effectiveness and value of enhancing Mobile Privacy on smartphone platforms. 38
  37. • Real-time tracking, filtering and enforcement • Eliminate or reduce false-positives through better management of variable-level tags • Integrated with Expert rating system (crowd sourcing) • Detection of bypass attempts 39
  38. • http://appanalysis.org/demo/TaintDroid_controller.swf 40
  39. nitisha@cmu.edu sean.wang@sv.cmu.edu jiang.zhu@sv.cmu.edu 41
  40. Thank you.
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