https://connpass.com/event/99461/ Short talk by Dr. Jörg Dörr, Fraunhofer IESE. Abstract: In the new world of Digital Transformation, cross-company and even cross-domain collaboration play a crucial role in creating new business value. As part of these new business models, data analysis and exchange across companies are essential, but questions of data protection sometimes hinder a successful establishment of these new business models. This talk presents typical situations we experience with companies in this context and introduces the mostly unknown concept of data usage control that extends the classical access control in order to enable new data-driven business models. Concrete project examples show how this concept was used for different domains and also cross-company collaboration (the PRO-OPT project).

1. FRAUNHOFER INSTITUTE FOR EXPERIMENTAL SOFTWARE ENGINEERING IESE
Dr. Joerg Doerr
Division Head
Information Systems
DATA USAGE CONTROL AS ENABLING FACTOR FOR NEW
BUSINESS MODELS IN CROSS-COMPANY VALUE CREATION

2. © Fraunhofer IESE
2
„Data is the new gold“
„Data is the new oil“
„Data is the new currency“
„Data is the fourth production factor“
…?

3. © Fraunhofer IESE
3
The value of data
Only those who
a) may use and
b) provide adequate protection
can make data permanently usable as a production factor!
 End users and business partners rather agree to the use if data
sovereignty and control over the data remains guaranteed.
self-determination
transparency

4. © Fraunhofer IESE
4
Source: https://www.cnet.com/news/whatsapp-sued-
german-watchdog-vzbz-privacy-concerns/
The value of data
Source: https://futurism.com/our-devices-are-spying-on-us-welcome-
to-the-internet-of-everything/

5. © Fraunhofer IESE
5
Situation in German Companies: Contrasts in Data Handling
Fort-Knox Solution
 protection of infrastructure and data
 secure
 inflexible
Open-Data Solution
 easily accessible data
 added value through data analysis
 potentially insecure
https://www.flickr.com/photos/suzannelong/3227461530https://www.flickr.com/photos/80038275@N00/20417718761

6. © Fraunhofer IESE
6
The Middle Way with Data Usage Control
Security control at every use at
runtime
Data itself is protected
Flexibly changeable at runtimeFort Knox
Solution
Open Data
Solution
Data Usage Control

7. © Fraunhofer IESE
7
Access Control vs. Usage Control
Access control is not enough!
Usage control – a generalization of access control
Fine-grained policies specify how data is handled
after access has been granted
Usage
Control
Access
Control
Provisions Obligations
Past +
Present
Future Usages
Obligation
Delete data after 3 days
Obligation
Do not forward
Roles
Risk Manager
Purpose
Risk Management

8. © Fraunhofer IESE
8

9. PRO-OPT
EXPLOITING HIDDEN DATA TREASURES IN
THE AUTOMOTIVE VALUE CHAIN
PRO-OPT
EXPLOITING HIDDEN DATA TREASURES IN
THE AUTOMOTIVE VALUE CHAIN
Dr. Jörg Dörr
Fraunhofer IESE
Dr. Jörg Dörr
Fraunhofer IESE
Funded by
PRO-OPT: Big Data Production Optimization in Smart Ecosystems 9

10. Motivation
Enable cross-organizational analyses in the automotive value
chain to increase product quality over its lifecycle
PRO-OPT: Big Data Production Optimization in Smart Ecosystems 10

11. Complex Product
ECU Manufacturer Module Supplier OEM Production Further LifecycleParts Supplier
Complex Ecosystem
PRO-OPT: Big Data Production Optimization in Smart Ecosystems 11

12. ECU Manufacturer Module Supplier OEM Production Further LifecycleParts Supplier
• ••
Diagnosis System
PRO-OPT: Big Data Production Optimization in Smart Ecosystems
Complex Product Complex Ecosystem
12

13. ECU Manufacturer Module Supplier OEM Production Further LifecycleParts Supplier
§
§
PRO-OPT: Big Data Production Optimization in Smart Ecosystems 13

14. ECU Manufacturer Module Supplier OEM Production Further LifecycleParts Supplier
§
§
High CostsDelayed Analysis
PRO-OPT: Big Data Production Optimization in Smart Ecosystems 14

15. ECU Manufacturer Module Supplier OEM Production Further LifecycleParts Supplier
Logical, secure, and performant integrated
data across the value chain
PRO-OPT: Big Data Production Optimization in Smart Ecosystems
High CostsDelayed Analysis
15

16. ECU Manufacturer Module Supplier OEM Production Further LifecycleParts Supplier
PRO-OPT: Big Data Production Optimization in Smart Ecosystems 16

17. Architecture of the PRO-OPT Plattform
Module Supplier
OEM ProductionECU
Manufacturer
17

18. Data Protection during Big Data Analysis
PRO-OPTCatalogueOEM
OEMPRO-OPT
Read warranty_claims_oem.csv → ProOptDataSet<String> input
GetSuspiciousFeatures(input, timerange) from CarFeatures→ ProOptDataSet<Feature> suspiciousFeatures
PRO-OPT
ProOptException → Size of Result Set to small
Data of Features→ Max Count of Requests per Day
Data of Features→ Max Size of Reference Set
performChecks→ NOK
Problematic Feature
 Windows with
noise insulation
Start of PRO-OPT-
Application
GetConcretePartTypeDistribution(input, PartType) from CarParts → ProOptDataTable<Object> parts
CSV
User
Data of Features → Min Size of Reference Set
Filter featuresDF by vins → featuresDF
Collect featuresDF → Row[] features
Read source CarFeatures → DataFrame featuresDF
Analyze(features, timerange) → Row[] suspiciousFeatures
existsWarrantyClaim for user→ true
Present Result
small timerangebigger
Data of Parts→ Pseudonymize Supplier
Data for supplier→ Claim exists
Adjust Time Range
performChecks→ OK
18

19. © Fraunhofer IESE
19
Conclusions
Data usage control enables data-centric business models
It is a necessity for trust between different companies in future
ecosystem, but also for trust from the people in our society!
It significantly increases the value of data
It can also be a unique selling point compared to competitors
Technical Solutions for data usage control like IND²UCE / MyData have to
be integrated into existing systems and software

20. © Fraunhofer IESE
20© Japanese Garden in Kaiserslautern, 2015
Partner City of Bunkyo-ku, Tokyo
Dōmo arigatōどうもありがとう