Jason McFall, CTO, Privitar, talks about how privacy engineering can make data analysis easier and more robust on the In:Confidence 2019 main stage (April 4th at Printworks, London).

1. Privitar.com
Regulationbreeds
innovation
How privacy engineering makes data analytics faster
and easier
Jason McFall, CTO

2. Privacy UtilityUsability

3. Privacy
Usability

4. 1 Faster Access to Data

5. No Yes

6. Privacy Engineering:Data Provisioning
Privacy Policy
Assembly
Metadata
Catalogue
ID Management and
Access Rights
Data Request
Service
DataSet
Directory
Raw Data
Privacy Policy
Execution
Secure Data Analytics Environment
Protected Data
Domain

7. 2 Data Science Efficiency

8. Celltowers
TheBritish Library
GreenPark South Bank
Sir John Soane’s Museum
Printworks
1/1/1910:35am
1/1/1910:07am
1/1/1911:12am
1/1/1910:44am
1/1/192:01pm
St. Paul’s Cathedral
1/1/1912:01pm
Location traces are highly sensitive, and hard to deidentify

9. Source: Crowdflower/FigureEight Survey of data scientists

10. # Calculate mean commute distance
def commute_distance(location_trace):
# evaluate brand loyalty
def switching_propensity(transactions_vendor):
# score braking safety
def commute_distance(brake_sensor_data):
# subsequence frequency
def count_repeats(motif,gene_sequence):
Approved
Standard
Functions
Sensitive
Raw Data
# Define evaluation function
def evaluation_function(attributes, classes, batch_size):
attributes=dict(attributes)
if classes is None:
inputs = attributes
else:
inputs = (attributes, classes)
dataset = tf.data.Dataset.from_tensor_slices(inputs)
dataset = dataset.batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:evaluation_function(test_x, test_y, 100))
Analysis code

11. 3 Machine Learning Robustness

12. Cat Dog

13. Overfitting to fine details in
the training set harmful

14. https://nypost.com/2017/11/08/these-adorable-cats-and-bunnies-are-purr-fect-doppelgangers/

15. https://nypost.com/2017/11/08/these-adorable-cats-and-bunnies-are-purr-fect-doppelgangers/

16. Error
Model training cycles
Stop here
DataTraining Data Test Data
Overfitting

19. 0.910.88
0.93 0.89
0.90
Papernot et al https://arxiv.org/pdf/1610.05755.pdf

20. Privacy
Utility

21. Summary
Deploying privacy engineering can make data analytics faster
andeasier
Faster Access To Data
MachineLearning
Robustness
Data Science
Efficiency

22. Privacy
Usability
Design
Privacy Engineering
Into Systems Early