a pattern catalog for gdpr compliant data protection · develop a stream analytics suite supporting...
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Chair of Software Engineering for Business Information Systems
Department of Informatics
Technische Universität München
DC
A Pattern Catalog for GDPR Compliant Data Protection
“Data Cooperatives”
„Privacy is the claim of individuals, groups or institutions to
determine for themselves when, how and to what extent information about them is communicated to others“ (Alan Westin)
€
Research goals
Alternative operating models for GDPR compliant data organizations
Data economy Regulation
We have come to expect Personalized services
require extensive personal data
With the use of smartphones, IoT devices and
personalized services, we leave vast amounts of
digital traces in the hands of companies
Regulators have the difficult task of balancing the
protection of individuals with the promotion of
technological & business development
Companies have to efficiently implement privacy
regulation
GDPR key elements
• New territorial scope,
definitions,…
• Extended rights for data
subjects: transparency,
portability, objection, notification
of data breach, rectification,
erasure,…
• Principle of accountability, data
protection by design and default
• Records of processing activities,
data protection impact
assessments
• Designation of Data Protection
Officer, certification
mechanisms
• Fines of up to 4% revenue for
non-compliance
Set of fundamental requirements Set of fundamental solution patterns
e.g. right to data
portability
Conceptual
Strategic
Organizational
Technical
Cultural
e.g.
require
portability
from
processor
e.g.
implement
export
functionality
Which conceptual frameworks can be
instrumented to describe regulatory
requirements and the organization of
possible solutions?
What are the elementary requirements of
the GDPR?
How is GDPR compliance achieved in
practice?
What is the value or effectivity of observed
solutions?
How can solution patterns be assessed
and how are they interrelated with each
other?
2
GDPR compliant storage of full genome, option to identify common
(family) predispositions in order to proactively work against them –
e.g. high blood iron
Collects user data from social media and stores it locally or in the
customer’s cloud storage
Uses anonymized trip data to provide information about traffic
density and estimated travel times between city areas
Provides a safe solution for members of the cooperative to store
their health data and optionally share it with research institutions
• How could an organization support data privacy?
• What would be feasible models? The “Data Cooperative” as an advisor to the
end user (1), as intermediary between services and the user (2), as a provider
of data protection services for companies (3) or as a provider of services to
the end user (4)?
• Who would initiate such an organization?
Dominik Huth
DC
DC
DC
On Google “my activity”, personal data can be viewed and deleted
in accordance with the current EU-US Privacy Shield
(4)(3)(2)(1)
Prof. Dr. Florian Matthes
5
5
4
4
3
1
1
2
Instantiated GDPR Project
e.g. update
privacy policy
3
MEGENO
Chair of Software Engineering for Business Information Systems
Department of Informatics
Technical University of Munich
Motivation
• The way we consume mobility is drastically changing
• Mobility is no longer only provided by traditional public
transport and goods we own, like cars and bicycles, but
also by service providers which offer mobility as a service,
like car sharing companies
• This new mobility ecosystem enables more flexibility, but
also introduces additional complexity, especially for
intermodal travel, i.e. reaching one’s destination by using
multiple means of transportation
• One of the main obstacles towards an integrated solution
of multiple mobility services is the lack of cooperation
between mobility service providers.
NLU Technology
Comparison of:
• Microsoft LUIS
• IBM Watson Conversation
• API.ai
• RASA (Open Source)
https://github.com/sebischair/NLU-Evaluation-Corpora
Approach
Approach
• Combine APIs of different mobility services
• Introduction of an abstraction layer in order to be
independent from service providers
• Creation of a central routing algorithm which enables
intermodal mobility
• Make it accessible through a chat bot to simplify the
planning process for users
Example Chat
This work has been part of the Vertical Social Software Project
and has been funded by Siemens Corporate Technology
Customer-Centered Intermodal Combination of
Mobility Services with Conversational Interfaces
[email protected], [email protected], [email protected], [email protected]
Daniel Braun, Adrian Hernandez Mendez, Manfred Langen, and Florian Matthes
Evaluation results
Context-AwareVerticalSocial
SoftwarePlatform
ConversationalInterface
NLU
Routing
MVG
DB
…
MVGConnector
DBConnector
… Connector
Chat
Chat
BotConnector
User
How can I get from München to Augsburg?
TravelCompanionBot
Take the 🚂 RJ 111 from München Hbf to Paris Est at 06:23. You will arrive at 06:54 at Augsburg Hbf.
User
From Garching Forschungszentrum to Flughafen
TravelCompanionBot
First, take the 🚍 Bus 230 from Garching, Forschungszentrum to Ismaning at 10:11. You will arrive at
10:29. Then, take the 🚆 S-Bahn 8 from Ismaning to Flughafen, Besucherpark at 10:42. You will arrive at
10:53. Your journey will take 🕜 42 minutes.
User
I want to travel from Boltzmannstraße to Neuperlach Süd
TravelCompanionBot
First, 🚶 walk to 🚈 U station Garching-Forschungszentrum. You will arrive at 14:06. Then, take the 🚈 U 6
from Garching-Forschungszentrum to Odeonsplatz at 14:06. You will arrive at 14:30. Then, take the 🚈 U 5
from Odeonsplatz to Neuperlach Süd at 14:37. You will arrive at 14:52. Your journey will take 🕜 50
minutes.
This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic
Affairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.
ACKNOWLEDGMENTS
Stream Analytics in IoT Mashup toolsIoannis Varsamidakis, Tanmaya Mahapatra, Ilias Gerostathopoulos, Christian Prehofer
{ioannis.varsamidakis; mahapatr; gerostat; prehofer}@in.tum.de
Software- and Systems Engineering
Approach
Big Picture
Scenario 1: Twitter Sentiment Real – Time Analysis
Objectives
Challenges
Enable stream analytics in an IoT mashup tool
No technical background needed to analyze streaming data
No coding skills needed to analyze streaming data
Support real-time stream processing
Support asynchronous and non-blocking stream processing
Parameterize stream processing properties through a user-
friendly UI
Implement a stream analytics suite & integrate it in a IoT
mashup tool
Support Content & Time-based Window processing
Support various overflow mechanisms (e.g. backpressure)
Simplified visual notations for specifying stream processing
properties
Support design & deployment of streaming processes for Spark
and Flink (Future Work)
Get all tweets for a specific topic (e.g. “Raspberry”) as a stream and calculate the sentiment of each tweet.
Then calculate the moving average of the sentiment of the topic and publish the results on a Raspberry-Pi
Stream analytics jobs are invoked to calculate the moving average for the sentiment of a twitter topic in
real time
Develop a Stream Analytics suite supporting various Stream Analytics functions (e.g. filter, merge) using the Akka Stream library, based on
the Reactive Streams specifications
Integrate the Stream Analytics suite in the aFlux IoT Mashup tool (multi-threaded, based on Akka’s actor system)
Development of a set of visual semantics to facilitate specification of stream analytics jobs within the IoT mashup tool
Development of various overflow strategies to ensure that the receiving side is not forced to buffer arbitrary amounts of data (overflow
strategies are defined by the user, e.g. back-pressure)
IoT Mashup
Tool
Stream Analytics
Real-time insights of data
Scenario 2: Stream Analytics on SUMO Traffic Simulator
Traffic Monitoring System records live traffic data and detects congestion scenarios. On detection of a new
congestion incident, a new lane might open to counter act the congestion
Stream analytics jobs are invoked to calculate congestion rates & trigger appropriate counter measures
Twitter StreamSA Moving
AverageSentiment MQTT Publisher
Kafka ConsumerSA Moving
AverageJSON Parser Kafka Producer
aFlux flow example:
aFlux flow example:
This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic
Affairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.
ACKNOWLEDGMENTS
Pricing Models of Shared Autonomous Vehicle SystemsAndreas Hein¹, Julia Veitl², Christopher Kohl¹, Lisa Kissmer², Helmut Krcmar¹
{andreas.hein; christopher.kohl; krcmar}@in.tum.de; {julia.veitl; lisa.kissmer}@bmw.de
¹Chair for Information Systems
²BMW Group
The Service Attributes
SAV Business Model
Towards a SAEV Pricing Model
Objective
Challenges Upcoming challenges & new requirements for new mobility service
providers (parking, charging time, social constraints, etc.)
Autonomous vehicles have a great disruptive & economic potential for
OEMs, however new entrants such as tech giants increase the
competitive pressure on established OEMs
Diverse and new customer needs require new business models such
as shared autonomous vehicle (SAV) systems
Research gap: Multicultural study about the willingness to pay for
shared autonomous electric vehicle systems (Krueger et al. 2016; Kockelman &
Quarles, 2018)
What are relevant service attributes for shared autonomous electric
vehicle systems (SAEVs) ?
Which customer-oriented pricing models for SAEVs could be
sustainable in the German as well as in the American market ?
Method
Qualitative in-depth interviews (Gläser & Laudel, 2010)
Quantitative customer survey using Adaptive-Choice Based Conjoint
Analysis (by Sawtooth Software)
Key Results*
Experts predict full acceptance of SAEV systems
Most important attributes
German market: reliability & safety
American market: safety & service quality
Willingness to pay for SAEVs will decrease compared to todays
mobility services
Coexistence of subscription models & pay-per-use options
Politics & regulations will play a major role in the future SAEV market
* Based on qualitative interviews, conjoint analysis is still in progress
Characteristics
Current on-demand mobility services (carsharing, ridesharing, taxi
services) as a foundation for first implementation of SAEVs
Service Attributes– The case of SAEVs
Current business models of mobility services
Research studies about
Sharing concepts
Electric and/or autonomous vehicles
Transportation choice
In-depth interviews with heavy users & mobility providers
Derivation of final service attributes for Conjoint Analysis
MOBILITY ON DEMAND SYSTEM WITH AUTONOMOUS VEHICLES
BookingVia
App, Call,
SMS
Users
Verification
Fleet
Management
System
Autonomous Vehicles
SERVICE ATTRIBUTES INFLUENCING WTP FOR SAEV SYSTEMS.
Willingness
to pay
Price (P)
Incentives
(Loyalty, Priority, Refer-a-friend) (P)
Availability (FC)
Cleanliness (FC)
Data Privacy (FC)
Safety (FC)
Support (FC)
Reliability (FC)
Ease of Use )EE)
Invoicing (EE)
Brand (HM)
Exterior (HM)
Engine (HM)
Interior (HM)
Vehicle Features (HM)
Use Case (H)
Convenience (PE)
ETA (PE)
Flexibility (PE)
Multimodality (PE)
Parking (PE)
Pooling (PE)
Waiting time (PE)
Image (SI)
Human interaction (A)
Politics & Regulations(A)
This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of EconomicAffairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.
ACKNOWLEDGMENTS
RoomR: Kick-starting Indoor NavigationNikolaos Tsiamitros, Efdal Ustaoglu, Georgios Pipelidis and Christian Prehofer{nikos.Tsiamitros, efdal.ustaoglu, georgios.pipelidis, christian.prehofer}@tum.deSoftware Engineering for Business Information Systems
Big Picture
Objective
Challenges
Approach
Results
Constructing accurate indoor maps to enable infrastructure
independent precise localization.
Devising a method to dynamically generate particles for the
particle filter to be used for localization.
Existing methods for localization cannot be used with the
available open map data.
Adjusting and expanding existing algorithms according to the needs
of our use case.
Use the geometry and other characteristics of indoor places to
deduce the location.
Crowd-source WiFi signal strength signals of the access points and
reason on them.
Provide a mapping framework that works transparently to create
high precision maps from unreliable sensor data.
I. Retrieve the indoor OSM
model and extract the
relevant map data.
II. Enhance the map with
particles and import it to
the particle filter.
III. Calculate the initial
direction of the user.
IV. Use an enhanced particle
filter with dead reckoning
to localize the user
I. Classify incoming data
based in their unique
properties.
II. Perform cluster analysis
to identify the number of
clusters.
III. Fuse all the data that
have been extracted
from the same regions.
IV. Train a classifier to
predict those locations.
We created an indoor
navigation app for the MI
building to demonstrate our
idea.
The user can find the location
of any room in the building on
an accurate map.
The route to the room from
the entrance is also
displayed.
The user can start navigation
at the entrance of the building
and localize himself during
the entire route.
Grammars
ParticleGeneration
OSMModel
InitialDirection
CurrentDirection
StepCounter
Localization
QuantifyConfidence
VisualizeLocation Classification
Cluster Analysis
WiFiAnalysis
GSM Analysis
Geom. Analysis
Fusion
Clustering
Labeled WiFi
Labeled GSM
Labeled Geom.
Labeling
WiFi Clustering
GSM Clustering
Geom. Clustering
Training
ClusteredData
RawData
This work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic
Affairs and Media, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.
ACKNOWLEDGMENTS
SMART CAMERA APP FOR ASSISTING VISUALLY IMPAIRED (SCAVI)Santhanakrishnan Narayanan, Georgios Pipelidis and Christian Prehofer
{Santhanakrishnan.Narayanan, georgios.pipelidis; christian.Prehofer}@tum.de
Masters Student – Transportation Systems
Objective A brief on Google Tango
TOF based infrared camera (IRS1645C 3D image sensor chip) to
perceive depth
Fish eye camera along with inertial measurement unit (combination
of gyroscopes and accelerometers) to track location (visual
odometry)
RGB camera to capture details like color and as the viewfinder for
augmented reality
Develop a mobile application using Google Tango API for
visually impaired humans to detect obstacles in the path of
the user and notify him/her
Advantages of our Method Point Cloud Example
Realtime depth estimation
Better accuracy (1% of the distance measured)
Good working range (indoors – 0.1 to 4m)
Usability possible in low or no light conditions (only
depth estimation)
Point cloud data
Pixel Matrix
Depth Image
Camera
Intrinsics
Depth Image
Aggregate pixels
based on depth
Estimate Obstacle
size
Constructing depth image from point cloud data for
better visualisation and obstacle detection
Detecting the obstacle type (static objects in same
depth plane and objects with varying depth like
staircase)
Estimating obstacle size
To be done
Following pixels not to be considered from the point cloud
Pixels corresponding to depth > 3m
Pixels at a distance > 0.5m to the left and right of the focal centre
of depth camera
Pixels 1m above the focal centre of depth camera
Tango point cloud dataCurrent Plan
X – Distance of the points along top-bottom plane
Y – Distance of the points along left-right plane
Z – Depth value perpendicular to the plane of the camera
C - Confidence value in the range of [0, 1] where 1 corresponds to
full confidence
* When the phone is held in portrait mode