Big Data and Privacy. Fritz Henglein Dept. of Computer Science, University of Copenhagen. Finance IT Day Riga,

Transcription

1 Big Data and Privacy Fritz Henglein Dept. of Computer Science, University of Copenhagen Finance IT Day Riga,

2 About me Professor, Programming Languages and Systems, University of Copenhagen Director, Research Center on Functional High- Performance Computing for Finance (hiperfit.dk) Chairman, Danish Innovation Network for Finance IT Google Fritz Henglein

3 Big Data? Lots: Contracts, payments, messages, transactions Even more: IP-packets, clicks, mouse moves, Rapid: Real-time news events, clicks, video Messy: Bits, web logs, JSON, XML, SQL, ad-hoc Predictive: Creditworthy, about to default, get a divorce, is pregnant, have an accident, getting breast cancer?

4 Good news: It works Powerful compute clusters Large-scale data analytics Sophisticated machine learning algorithms

5 Big Data: Example

6 Big Data: Example

7 Privacy? Right to be left alone Ownership of and control over use of sensitive data

8 Bad news: Hard to attain Sloppy programming Unsafe programming languages and tools Security attacks (MiM, malware, social engineering ) Privacy attacks: Fingerprinting Correlation

9 Fingerprinting

10 Fingerprinting Your browser leaks 20+ unique bits in private browsing mode (Not bad news if you are on the server side)

11 Correlation Anonymized Netflix data IMDB data Anne Bob

12 Big Data + Privacy Wanted: Having a cake and eating it, too: Use aggregated sensitive data to predict ( learn ) Protect individual sensitive data The vortex: Simultaneously HOW? maximizing actionable ( predictive ) knowledge minimizing divulging private information

13 Data security: Not enough Encryption: Great: separation of key and data Not so great: need to decrypt to compute with data Physical security: Great: don t need to trust cloud provider, NSA, etc. Not so great: physical limitations on information propagation how to get information ``out?

14 Needed: Program security Techniques with mathematical guarantees for security and privacy Secure Multiparty Computation (SMC) Language-Based Security (LBS) Differential Privacy Data Provenance

15 SMC Multiple parties compute joint result without revealing ( leaking ) their individual inputs to each other Related to Zero-Knowledge Proofs: Prove you are over 18 without telling me your age Prove you are not a prime number without telling me your factors Assumes that parties are not colluding

16 SMC example: Mortgages for Danish farmers Source: Kurt Nielsen, UCPH

17 LBS Programs that guarantee that sensitive data are not leaked By analysis: programs are automatically analyzed and transformed before execution (information flow analysis) By construction: leaky programs cannot even be constructed (type systems) Example: ``sanitizing web code (e.g. against SQLinjection and cross-site scripting attacks)

18 Differential privacy Add statistical noise to query results Aggregate results approximate, but still useful Individual data protected Avoids incrementality attack: Central database: # of people in a region hitherto granted a loan Did Joe get the loan at the other bank? Query database with Joe s region on successive days

19 Data provenance Related to LBS Color sensitive data: Retain color(s) during computation: if {cpr1==cpr2} return 1 if {cpr1==cpr2} return 1 Monitor outputs: 1 is red, so the computation has used

20 Privacy and Regulation Security and privacy are not properties of data, but of the information that is computed from them and to whom that is transmitted. Data privacy is a red herring Should IP-addresses be private, but MACs not?

21 Take-away Big Data: Yes, it is hyped. But it is coming. (At least to your competitors.) Security and privacy: Don t just stare at your data. Look at your programs. vs Ask for mathematical guarantees. (If you need a lawyer, you didn t get any.)

22 Thank you!