Slides of Learning Without Peeking at SSBSE 2018

1. Learning Without Peeking:
Secure Multi-party Computation Genetic
Programming
Jinhan Kim, KAIST
Michael G. Epitropakis, Lancaster University
Shin Yoo, KAIST
SSBSE 2018
Montpellier, France

2. Genetic Programming
• GP has been successfully applied to various domains.
Data Security
Test coverage data
Defect proneness data
Internal resources

3. Historical
defect proneness
data
Algorithm
(GP)
We want to
analyze our data,
without revealing
any sensitive data
Secure Multiparty Computation (SMC)

4. Example
Users don’t trust each other,
but they want to achieve a common goal.
Alice Bob
I don’t
trust Bob
I don’t
trust Alice

5. Example
Alice Bob
A = 1 if Alice loves Bob
0 otherwise
B = 1 if Bob loves Alice
0 otherwise
Common goal: A & B
Without trusted third party, we can do this by using
Secure Multiparty Computation (SMC)

6. Secure Multiparty Computation
• 1-2 Oblivious Transfer
• Garbled Circuit

7. 1-2 Oblivious Transfer
Alice Bob
Message m0, m1
Random bit b
C = m0 if b = 0
m1 if b = 1
Alice (sender) has no idea
what message has been sent.
Bob (receiver) don’t know
the other message.
The 1–2 oblivious transfer protocol can be implemented over
asymmetric cryptography, such as the RSA.
enctypted

8. Garbled Circuit
Alice
(garbler)
Bob
(evaluator)
Alice garbles (encrypts) the circuit,
and sends with her input (encrypted)
① Bob evaluates (decrypts) the circuit,
using oblivious transfer
②

9. Obliv-C
• Obliv-C is both a domain specific extension of C and a gcc
wrapper that compiles the extension.
High-level interface for 2PC
Extension of C

10. Obliv-C Example
Variables that should
remain oblivious.
The body of obliv if will be
always executed.①
②
①
②

11. Secure Multiparty Computation GP
GP
Obliv-C
Fitness evaluation Data

12. Scenario 1: Multiparty Dataholder (2PC)
Obliv-CData Party 1 Data Party 2
GP Party
Fault info
(Jan 2018 - Mar 2018)
Fault info
(April 2018 - June 2018)

13. Scenario 2: Singleparty Dataholder (1PC)
Obliv-CData Party GP Party

14. Study Subjects
• Symbolic Regression using GP
• Keijzer-6
• Nguyen-7
• Vladislavleva-4
• Dow Chemical, Chemical process dataset
• Learning to Ranking using GP
• FLUCCS, Real-world fault dataset from Defects4j

15. RQ1: How well does the SMCGP perform
compared to the Normal-GP?
SMCGP and Normal-GP have
same logic, do same job

16. RQ1: Symbolic Regression
There is no statistical difference
between SMCGP and Normal-GP
For 2PC, we split dataset in half

17. RQ1: FLUCCS
Localize faults in Mockito-1 is
particularly challenging
Learning from 32 versions, we
tested on four versions.

18. RQ2: What is the runtime overhead of
SMCGP when compared to Normal-GP?

19. RQ2: Runtime Overhead
We observed that SMCGP is about 1,290 times slower than Normal-GP