1. Dan Boneh dabo@cs.stanford.edu with Monica Lam, David Mazieres, John Mitchell, and many students. Security for Mobile Devices NSF Site Visit, June 2010 POMI 2020

2. POMI Research Agenda Applications Data & Computing Substrate PrPl, Junction and Concierge Radio technology Economics Cinder: Energy aware, secure OS secure apps UI HW Platform Network Substrate Software Defined Network & OpenFlow Handheld Infrastructure

3. platform security secure apps POMI mobile security work Snap2Pass and Snap2Pay [DSBL’10] A password manager for mobile devices [BBBB’09] Android security: ASLR on Android [BB’10] Unlocking phones using cheap tokens [BB’10] Preventing tap-Jacking attacks on mobile web sites [RBB’10]

4. Joint work with Arvind Narayanan, Narendran Thiagarajan, and Mugdha Lakhani Location services without big brother

5. Location-based social networking Finally taking off?

6. Proximity Alerts Detect when friends are nearby (e.g. Loopt) Today: 24/7 user tracking by server Our privacy goals: When not nearby, friends don’t see your location Server never sees your location Building block for more complex functionality

7. Proximity alerts: applications Granularity must be user-configurable

8. How we arrived at this problem POMI barrier #1: reliance on big brother PrPl effort: social networks with privacy Many discussions with PrPl participants: Can we make location-based services private? Similarly, can we do private targeted advertising? (NDSS’10) Other results from the interaction: QR codes for better user authentication [DSBL’10] Unlocking a phone using cheap tokens [BB’10]

9. Reducing proximity test to equality test

10. Equality testing Space of possible locations is small! (32 bits) Method 1: protocol based on public-key encryption (Lipmaa) Heavy computation: impractical for proximity of all friends xy = ? Requires shared secret keys between pairs of friends

11. Our approach An efficient protocol with server participation Trust assumption: server does not collude with your friends x y r ( x – y ) Total traffic: 24 bytes, easy computation ?? no one knows r

12. Problem: online brute-force attack If only there were a way to verify that a user really is where they claim to be… Solution: location tags (for small granularity)

13. Properties of location tags Location tag = vector + matching function i.e., space-time fingerprint Unpredictability cannot produce matching tag unless nearby Reproducibility two devices at same place & time produce matching tags (not necessarily identical)

14. Location tags using WiFi packets Discard packets like TCP that may originate outside local network DHCP, ARP, Samba etc. are local 15 packets/sec on CS/EE VLAN Two different devices see about 90% of packets in common Comparing location tags: privately test if intersection > 90%

15. Android implementation

18. Future work Many location privacy questions: Private location based advertising Private location based search Private location statistics