Re-identification of Anonymized CDR datasets Using Social network Data

Alket Cecaj, Marco Mamei, Nicola BicocchiUniversity of studies of Modena and Reggio Emilia

PerCom 2014

IEEE International Conference on Pervasive Computing and Communications. Budapest, Hungary

More data..big opportunities of study

Dataset join and privacy issues

• Matching different users associated to the same real person.

• Privacy issues: any kind of information can be inferred

● Join different datasets is the key for advanced forms of context awareness

Related work Anonymization.. and re-identification• Gender, ZIP and full date of birth 63% of re-identification

• movie ratings from NetFlix Prize dataset

• Medical records of Massachusetts Hospital using a voters list

• re-identification of anonymous volunteers in a DNA study for Personal Genome Project

In line with our domain• Unique in the Crowd: the privacy bounds of Human Mobility

• Markov chain models for de-anonymization of geo-located data

Dataset join and privacy issues.

• Can we use data from social networks to re-identify users for an anonymized dataset such as a CDR one?

• Probabilistic approach to evaluate the re-identification potential.

CDR and Social Data sets

CDR and Social Dataset - Distribution of events● CDR● on average 28 events/period , max = 330, min = 3● 2.019321 users for final analysis● Social dataset● on average 20 events/period , max = 424, min = 3● 700 users for final analysis

Matching users among datasets● Time and space parameters for matching for example 10min of time

interval between events and cell radius as physical distance

● Clone of social dataset in order to check/verify the quantity of matchings that were done by chance following Bonferroni’s principle.

● Exclusion of CDR users making events in the same time but in a long distance much bigger that the cell radius.

Convergence to one ?

Distributions and Percentages

Probabilistic modelling Given FTa, U discrete random variable, having NU values Ui

i= 1...N

Overall results

ConclusionsPotential and/or limits of re-identification of users across multiple mobility datasets.

Future research:• the current model and overall approach needs refinement

• privacy concerns though mechanisms for preserving privacy and data utility for a single aspect

• correlation among data sets represents a big opportunity to enrich the information available to a pervasive application

Thank you for your attention. Questions are welcome.