Ethical Design of Location-Data-Driven Applications
Strategies to protect user privacy
Definition of Location Data
Photo by PhotoMIX Company: https://www.pexels.com/photo/close-up-of-the-maps-on-a-smartphone-5921677/
What is Location Data
Lng/Lat points or traces
Semantic Locations
Categorical Locations
Areas
S2 Cell
Forms of Location Data
IP address
Photos
Click on ads
Learning Objectives
At the end of the training you will be able to:
1. Identify optimal design solutions that are feasible and comply
with the company’s policy about user privacy
2. Apply strategies to protect user privacy
Learning Outcomes
At the end of the training you will have created:
1. A data-set file with the appropriate changes to design a
solution to a given problem
2. A code file with data input and data output for analysis
Session 1
Identify the Appropriate Design Solution
The Locus Charter Principles &
General Data Privacy Regulation
Realize opportunities for social and economic benefit responsibly.
Understand impacts: who (individuals or groups) and what
Lawfulness of data processing
Purpose limitation
Data minimization
Storage limitation
Avoid data discrimination, data exploitation, and intrusion
Pair Weighting Procedure to Prioritize the Design Ideas
1. Compare each solution idea with the rest as to feasibility,
acceptability, and the company’s policy about user privacy.
2. Circle the solution idea of the two that is more feasible and
acceptable.
3. Count the circles for each solution idea and prioritize them.
Quiz Time
Session 2
Strategies to Protect User Privacy at the Prototype Stage
Data Collection & Processing Principles
Protect user privacy
Protect user identification
Identify Risks in Data Collection and Data Process
Location data tied to the user
Location data that imply user presence
Data that imply specific location (sensitive locations)
Sequence of locations (it can be a fingerprint of a user)
Search impression identify specific location
Strategies of Data Collection and Data Process
Data set selection
Data selection: delete attributes of locations, minimize identifiers
Data encryption
Clustering: use coarse data
Consider breaking sequences
Anonymization techniques
Group Discussion