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. Digital Security Physical Security Data Platform (BOSS) Data Visualization Apps Data Sources Control Data Data Learning Apps How can we ensure sandboxed data analysis? How can we restrict mixing of data? How can we understand arbitrary data types? How can we ensure the reliability of control data? How can we improve the integrity of 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?? How do we make sense of the wide variety of data sources? Who has access to what data? Is the building a natural boundary for data?

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

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

19. TLS Proxy Secure Block Device Storage TPM Chip (Remote Attestation) Linux Kernel IPTables Controller ACL Stor e ACL changes Ether Pad Friend Share Application Layer K/V Proxy FS Proxy DeDup Storage Layer End Users 19

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

21. Web Frontend Data Set Manager 1. Data Set 2. Data Parser 3. Privacy ↵ Budget (ε) Isolated Execution Chambers Computation Manager Untrusted Computation Comp Mgr XML RPC Layer Computation Differentially Private Answer Noise Generator 1. Computation 2. [Bounds Estimator] Auditing