Confidential Computing - Analysing Data Without Seeing Data
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How can people collaborate over data analysis without disclosing their data to each other? This seminar will cover an end-to-end solution to this problem, including privacy preserving entity resolution and the application of partial homomorphic encryption and Rademacher observations to private linear classification tasks. In particular we will show that it is possible to learn from data, while keeping the data confidential, both with and without the entity resolution step. We will give a brief overview of potential applications and give some practical examples of how these approaches can be used.
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Confidential Computing - Analysing Data Without Seeing Data
- 2. Future Value of Data Data Analytics Without Seeing the Data 2 | time value release Data decays with time!
- 3. Future Value of Data Data Analytics Without Seeing the Data 3 | time value release Joined with another data set – more value!!
- 4. Future Value of Data Data Analytics Without Seeing the Data 4 | time value release New analytics techniques – more value!!
- 5. Future Value of Data Data Analytics Without Seeing the Data5 | time value release Data decay + Joining new data + New analytics techniques Uncertain future value Unknown future risk
- 7. The Problem How can we learn valuable insights from sensi1ve data from mul1ple organisa.ons? Insights Sensitive data Sensitive data Joint Analysis Confidential Confidential Data Analy.cs Without Seeing the Data 7 |
- 8. Three Basic Building Blocks • Private computa.on • Arithme.c on encrypted numbers • Distributed, confiden.al analy.cs • Distributed algorithms, computa.on & protocols • Private Record Linkage • Privacy preserving record level matching Data Analy.cs Without Seeing the Data 8 |
- 9. Solu1on (1): Private computa1on 3 E 71175935987496430338623223060201843925208459762815635262949815592595 16861516633702469933935260534155369128712003211669147527394965883186 98743040588706948658192655353713280945959536474253285115856347911583 77797185627083578174160157299579445890692023902698424427665636040729 38327792655060957281939887206011322264791188672934779233385835564950 538042608146734818512597109….......... 65535371328094595953647425328511585634791158377797185627083578174160 15729957944589069202390269842442766563604072976104138715920619699952 17697451818900805720754176976456091364980410538327792655060957281939 88720601132226479118867293477923338583556495053804260814673481851259 70093558089132685793389213865608731685640953069735077874534452166343 33195600873200349632089….... 2 E + “+” 95364742532851158563479115837779718562708357817416015729957944589069 20239026984244276656360407297610413871592061969995217697451818900805 11886729347792333858355649505380426081467348185125971095628099782109 58956224480113528398128884692700462576308469655060770093558089132685 79338921386560873168564095306973507787453445216634333195600873200349 632089270046257630846….... D 5 = = Data Analy.cs Without Seeing the Data 9 |
- 10. Solu1on (1): Private computa1on 3 E 71175935987496430338623223060201843925208459762815635262949815592595 16861516633702469933935260534155369128712003211669147527394965883186 98743040588706948658192655353713280945959536474253285115856347911583 77797185627083578174160157299579445890692023902698424427665636040729 38327792655060957281939887206011322264791188672934779233385835564950 538042608146734818512597109….......... 65535371328094595953647425328511585634791158377797185627083578174160 15729957944589069202390269842442766563604072976104138715920619699952 17697451818900805720754176976456091364980410538327792655060957281939 88720601132226479118867293477923338583556495053804260814673481851259 70093558089132685793389213865608731685640953069735077874534452166343 33195600873200349632089….... 2 E + “+” 95364742532851158563479115837779718562708357817416015729957944589069 20239026984244276656360407297610413871592061969995217697451818900805 11886729347792333858355649505380426081467348185125971095628099782109 58956224480113528398128884692700462576308469655060770093558089132685 79338921386560873168564095306973507787453445216634333195600873200349 632089270046257630846….... D 5 = = 10 | Data Analy.cs Without Seeing the Data
- 11. Solu1on (2): Distributed analy1cs Compute Data Dept 2 Compute Data N1 Secure compute Confidentiality boundary Data always remains confiden1al to the source ins.tu.on Dept 1 Compute N1 Coordinator Messages containing encrypted data 11 | Data Analy.cs Without Seeing the Data
- 12. Solu1on (3): Private Record Linkage Dataset A Dataset B Tori Mckone 7/06/1921 F Tori Mackon 6/07/1921 F Victoria Mckon 7/06/1921 F ? ? 12 | Data Analy.cs Without Seeing the Data
- 13. Use Cases
- 16. Industry using Gov Data Model Builder Own Data Gov Data 17 | Data Analy.cs Without Seeing the Data
- 18. Device Analy1cs Data Analytics Without Seeing the Data Model of normal behaviour OK OK NG OK Private Modeling learn deploy OK NG OK 19 |
- 20. Homomorphic encryp1on Partial Homomorphic Encryption Somewhat Homomorphic Encryption Fully Homomorphic Encryption Allows either addition or multiplication of encrypted numbers Allows evaluation of low order polynomials Allows evaluation of arbitrary functions Moregeneral Faster Data Analy.cs Without Seeing the Data 21 |
- 21. Paillier Encryp1on c = gm rn modn2 Encryption of m: D E m1( ).E m2( )modn2 ( )= m1 + m2 modn D E m1( ) m2 modn2 ( )= m1m2 modn Addition of encrypted numbers: Multiplication of encrypted number by a scalar: Data Analy.cs Without Seeing the Data 22 |
- 22. Paillier Encryp1on c = gm rn modn2 Encryption of m: Addition of encrypted numbers: Multiplication of encrypted number by a scalar: gm1 × gm2 = gm1+m2 gm1 ( ) m2 = gm1m2 Data Analy.cs Without Seeing the Data 23 |
- 23. Paillier Implementa1ons • Python – open source • www.github.com/nicta/python-paillier • Java – open source • www.github.com/nicta/javallier • Javascript – s.ll under closed development 24 | Data Analy.cs Without Seeing the Data
- 25. Distributed Compu1ng with a Twist Compute Data Org 2 Compute Data N1 Secure compute Confidentiality boundary Data always remains confiden1al to the source organisa.on Org 1 Compute N1 Coordinator Messages containing ONLY encrypted data Data Analy.cs Without Seeing the Data 26 |
- 26. Graph Computa1on Engine Domains CE CE CE DF DF CE DF CE Coordinator Worker Workers Properties M M M M M Messages M JSON Message CE AKKA actors DF Data frames 27 | Data Analy.cs Without Seeing the Data
- 27. N1 Analy1cs Pla[orm Privacy Technologies Partial homomorphic encryption Private Record Linkage Irreversible aggregation Distributed Graph Computation Engine Analytics Statistics Regression Clustering Data Auth Machine Learning Learn Evaluate Deploy Network Data Analy.cs Without Seeing the Data 28 |
- 28. Logis1c Regression p x;θ( )= 1 1+e−θ.x L θ( )= yi logp xi;θ( )+ 1− yi( ) i=0 n ∑ log 1− p xi;θ( )( ) Logis.c func.on Log likelihood Minimise for : Evaluate: θ Requires “secure log” and “secure inverse” protocol using Paillier encryp.on 29 | Data Analy.cs Without Seeing the Data Builds on Han et al. 2010 “Privacy Preserving Gradient Descent Methods”
- 29. Example Paillier Logis1c Regression Org B CECE Coordinator Worker Secure Log Logistic Learner Secure Inverse M JSON Message CE AKKA actors DF Data frames Gradient Descent Private key holder Features & labels Features Org A N1Analytics 30 | Data Analy.cs Without Seeing the Data
- 30. Performance • Learning • Learnt models have the same accuracy as unencrypted calcula.ons • “Private learning” is (1000x) slower due to encrypted computa.ons. Learning .mes are several hours. • Deployment • A score can be generated in real .me (<50ms) • Customer data that contributes to the score remains private. ��� ���� ���� ��������� (����) ��� ���� ���� ��� �������� ���� (�) �������� �������� ���������� ������� ���� ��� ���� 31 | Data Analy.cs Without Seeing the Data
- 31. Scaling Coordinator Data Provider 1 Data Provider 2 Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker Worker �������� ● ● ● ● ● ■ ■ ■ ■ ■ ◆ ◆ ◆ ◆ 0 100 200 300 400 Cores 5 10 50 100 500 Minutes Learning time scaling ● 10,000x10 features ■ 100,000x10 features ◆ 1,000,000x10 features 32 | Data Analy.cs Without Seeing the Data
- 33. Record Linkage Challenge Dataset A Dataset B Tori Mckone 7/06/1921 F Tori Mackon 6/07/1921 F Victoria Mckon 7/06/1921 F ? ? 41 | Data Analy.cs Without Seeing the Data
- 36. Private Record Linkage 44 | 44 Organisa.on B Fuzzy Matcher Organisa.on A N1 Analy.cs A's$PII$data Name DOB Gender John/Smith 12/01/82 M Mark/Gorgon 1/12/90 M Hanna/Smith 4/02/78 F … … … … … … Juliet/Baker 2/11/72 F B's$PII$data Name DOB Gender Mark.Gorgon 1/12/90 M Juliet.Baker 2/11/72 F Andrew.Roberts 4/02/93 M … … … … … … Hanna.Smith 4/02/78 F A's$Cryptographic$Hashes Row Key 1 10110110...00101010 2 01110110...11010101 3 10011001...10100110 … … … … 100000 01101011...00101101 B's$Cryptographic$Hashes Row Key 1 01110110…11010101 2 01101011...00101101 3 01111000…00110011 … … … … 100000 10011101...10100111 Shared Secret Salt Hasher Hasher Linkage(Table Row$A Row$B 1 X 2 1 3 100000 … … … … 100000 X Similar in approach to MERLIN - Ranbaduge, Vatsalan, Christen (2015) Data Analy.cs Without Seeing the Data
- 38. Classifica1on without iden1ty linking 46 | Features Labels Rados Features Shared feature Labels* Label Propor.ons Learning from Label Proportions Patrini, Nock, Caetano, & Rivera, NIPS (2014), (Almost) No label no cry Data Analy.cs Without Seeing the Data
- 39. Classifica1on without iden1ty linking 47 | Features Labels Rados Features Shared feature Labels* Encrypted Label Propor.ons Learning from Encrypted Label Proportions Data Analy.cs Without Seeing the Data
- 40. Current Status
- 41. Current Capabili1es of N1 pla[orm • Standard data analy.cs techniques on confiden.al data: • Correla.on analysis • Classifica.on / predic.on • Regression • Clustering / outlier detec.on • Automated private record linkage • Fine grained authorisa.on and access control Dept 1 Org 2 Comp3 Private record linkage Sta.s.cs Classifiers Anomaly Detec.on Private analy.cs Federated model – No central database Data is kept local to the source 49 | Data Analy.cs Without Seeing the Data
- 43. Acknowledgements 51 | Engineering Mr. Brian Thorne Dr. Mentari Djatmiko Dr. Guillaume Smith Dr. Wilko Hanecka Dr. Hamish Ivey-Law Research Dr. Richard Nock Mr. Giorgio Patrini Dr. Roksana Borelli Dr. Arik Friedman Prof. Hugh Durrant-Whyte Business Mr. Warren Bradey Ms. Shelley Copsey Lead: Dr. Stephen Hardy Data Analy.cs Without Seeing the Data