The document discusses digitalization, data science, artificial intelligence, and privacy-enhancing technologies. It provides an overview of key topics such as the Internet of Things and the data deluge, trends in artificial intelligence, and examples of data usage in mobility analytics and maritime transportation in Norway. The document also discusses traditional privacy-enhancing techniques and emerging techniques for privacy-preserving machine learning.

1. Big Data, Artificial Intelligence, and
Data Science... What, why, how,
when!?
On the benefits and potential risks
JUC NETTVERK I PERSONVERN
2019.08.29
Arturo Opsetmoen Amador

2. Outline
Digitalization and its consequences
• What is digitalization
• IoT and the data deluge
Data science and its consequences
• Science in Data Science
• “Good science / bad science”
Examples in Norway
• Mobility Analytics • Maritime data
“Traditional” privacy enhancing technologies
• What we all have heard of: • Is it good enough?
2018, the year of privacy
• Is everything lost?
• Emerging technologies
• What happens when data is “too anonymized”
• The ongoing AI revolution

3. Digitalization and its consequences

4. 4Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
Mainly about three things
NEW processes NEW technologies NEW people
What’s up with digitalization
New ways of
collaboration
New role rotation
routines
New continuous learning
and development
New sensors on
everything!
New CV technologies
New location data
New Data Science
New workforce
New skill sets
New roles
New prof. profiles

5. 5Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
Welcome to the sensor revolution

6. Internet of Things
12 %
Average yearly increase in the number
of connected IoT devices
17 billion
Number of connected IoT devices in 2017 …
It didn’t get there… 10 by 2020
Now, 22 billion
in 2025
Will it get there?

7. 7Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
Welcome to the sensor revolution

9. https://bigdata-
madesimple.com/how-bad-
data-changed-the-course-
of-history-infographic/
How bad data
has changed the
course of history
http://www.enforcementtr
acker.com/
GDPR
enforcement
tracker

10. Data Science and its
consequences

11. Click to insert title
Click to insert presenter, location, and
date

12. 12Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
The inductive argument for “good science” With great power comes great responsibility
Two facets of science

13. 13Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
Trends in Artificial Intelligence

14. 14Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
IoT + AI = Sensor Fusion
Artificial Intelligence
Supervisor
Car: Sensor Fusion
Sensor Validation
In-Car Sensor
Acceleration Sensor
Ambient Sensor
Side Sensor
Pressure Sensor
Speed Sensor
Vision Sensor
Rear Distance Sensor
Vision
Hearing
Touch
Smell
Taste
Balance

15. 15Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
Generative Deep Learning

16. Examples in Norway

20. Mobility Analytics
and trends in
Artificial Intelligence
Port demand
prediction
• Use RNN to predict port
demands/saturation
• Given enough historical data, we can
utilize deep learning for route
prediction
Travel time and
distance
• Spatio Temporal-Neural Networks offer a
framework to predict travel distances
between ports
• Travel time can be predicted (Time of day,
atmospheric conditions, etc.)
Route optimization
• Deep Reinforcement Learning. By
observing reward signals and following
feasibility rules…

21. Encryption
For security reasons data should be encrypted. Both data
on transit and at rest. This imposes severe penalties in
performance
Masking
Masking techniques, such as hashing can be used
instead of encryption, they might be a good
compromise between security and performance
Extrapolation
In some cases, it is possible to extrapolate
from a customer base to a population
estimate. This introduces uncertainties but
increases protection of privacy
Path obfuscation
By introducing pseudo-random noise, we can
further protect privacy from the risk of re-
identification by inference. This will decrease data
quality
Privacy
Enhancing
Technologies
Aggregation algorithms
Aggregation techniques such as k-anonymity can
strengthen privacy frameworkds by avoiding exposure of
individuals. See l-variety, t-closeness...

22. Data enrichment:
Mobility meets
wastewater…
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Amphetamine
• Measured the mobility behaviour of people
in Oslo
• Correlated population dynamics with drug
consumption measurements
• Discovered the best time of the year to run
anti-drug campaigns

23. Assessing Alternative
Population Size Proxies
in a Wastewater
Catchment Area Using
Mobile Device Data
• Measured the mobility behaviour of people
in Oslo
• Correlated population dynamics with drug
consumption measurements
• Discovered the best time of the year to run
anti-drug campaigns

24. Recovering trajectories from Ash
Spatial aggregation - Tesselation Source – Individual trajectories Occupancy matrix
What happens after i time steps? The cost of moving Cost matrix

25. Testing of
a privacy
framework
Extremelly dificult to recover trajectories!
• Took measurements of mobility in Stavanger
• Around 100,000 individual trajectories
• Used a very good privacy framework
• Just a few trajectories were recovered

26. Maritime transportation
provides the best method to
transport goods over large
distances
The overall volume of trade by
this means is steadily growing
every year
Concerns in maritime safety
and security are growing
together with the industry!
Big Data
technologies for
intelligent maritime
navigation

27. Analytics for
intelligent
maritime
navigation
Port demand
prediction
• Use RNN to predict port
demands/saturation
• Given enough historical data, we can
utilize deep learning for route
prediction
Travel time and
distance
• Spatio Temporal-Neural Networks offer a
framework to predict travel distances
between ports
• Travel time can be predicted (Time of day,
atmospheric conditions, etc.)
Route optimization
• Deep Reinforcement Learning. By
observing reward signals and following
feasibility rules…

28. Traditional PETs

29. Encryption
Hashing
K- anonymity
L-diversity
PETs, the
usual
suspects
T- closeness
Not safe against
Deep Learning
attacks

30. Privacy preserving machine
learning

31. 31Presentation Title | Author | Date © 2017 Capgemini. All rights reserved.
The year of ML and privacy
Private AI resources
• https://github.com/Open
Mined/private-ai-
resources
FHE + diff. priv GAN private nets