Security and privacy in the age of software controlled surroundings

1. Security and privacy in the age of software controlled surroundings Prashanth Mohan David Culler

2. What are your expectations of privacy and security when you are in a shared space?

3. Changing the way we interact

5. In a personalized world

6. A living and breathing surrounding

7. No more key chains or key cards

8. How can we ensure sandboxed data analysis? How can we restrictmixing of data? Data VisualizationApps Data LearningApps How can we understand arbitrary data types? Data Platform (BOSS) Digital Security Physical Security How can we ensure the reliability of control data? How can we improve the integrity of data sources? Control Data Data Sources

9. Enforcing end-to-end user policies • Mobad- How can we maximize benefit while analyzing data locally (for privacy)? • Rubicon - Can we reuse existing software systems while still obtaining privacy guarantees? • Gupt - How can we mine data without divulging the privacy of individuals?

10. Many open privacy questions Can we describe privacy in higher level constructs?? Who has access to what data? Is the building a natural boundary for data? How do we make sense of the wide variety of data sources?

11. Security of building networks

12. Static Analysis Techniques Code Instrumentation Dynamic Analysis using Input Replay

13. Brainstorm: Ensuring security • Secure the networks! • Understand the state machine of the building – “control transactions” limit bad states • How can we apply the principle of least privilege for apps on BOSS? • Software security at the firmware layer

14. Thank You Prashanth Mohan https://www.cs.berkeley.edu/~prmohan prmohan@cs.berkeley.edu

15. Backup

16. Topics for discussion • When you enter a public building, what are your privacy and security expectations? • How expensive should attacks become in order to limit malicious behavior? • Is privacy a lost cause? • How much of these problems can be solved with appropriate regulation?

17. Research Progress Cloud Data Privacy: IEEESP13*, SIGMOD12 Client Device Web Application User data Machine Learning Multiple users’ data Processeddata Learning Models Client Data Privacy: EuroSys13, HotSec12, MobiSys10

18. Functional Blocks User Authentication Template Processor ACL Checking Isolated Containers Integrity Checking Easy drop-in solution for existing 3-tier programs Image source: Wikipedia

19. End Users ACL changes TLS Proxy TLS Proxy ACL Store Controller ApplicationLayer FriendShare DeDup EtherPad StorageLayer K/V Proxy FS Proxy Storage IPTables Linux Kernel Secure Block Device TPMChip (Remote Attestation)

20. Differential Privacy Privacy budget Neighbors: two datasets differing in exactly one entry Any measurable set Randomized algorithm Function Sensitivity

21. Differentially Private Answer Computation Web Frontend Noise Generator Data Set Manager Computation Manager 1. Data Set2. Data Parser3. Privacy ↵Budget (ε) Auditing 1. Computation2. [Bounds Estimator] Comp Mgr XML RPC Layer Isolated Execution Chambers Isolated Execution Chambers Isolated Execution Chambers Untrusted Computation