Privacy Challenges of Telco Big Data
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1 Dr. Günter Karjoth June 17, 2014 ITU telco big data workshop Privacy Challenges of Telco Big Data Mobile phones are great sources of data but we must be careful about privacy 1 / 15
2 Sources of Big Data Big data from cheap phones location data (cell, GPS) search queries s, Twitter, social networks Source: villevivante.ch 2012 Swisscom Ville Vivante, mobile data from the city of Geneve 15 million movements measurable from 2 million cell phone calls made on the Swisscom system during one day 2 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
3 Telecom Operators Benefits Data Trove telecom operators more and more treat customer data as an asset to be mined instead of a mere incidental to running networks Selling data on mobile users locations, movements, and web browsing habits may grow into a multi billion-dollar market Telecom big data products my be helpful well beyond the realms of advertising for credit card companies wanting to detect fraud, for ambulance operators plotting routes to avoid traffic,... Examples of data disclosure 2012 Orange Data for Development" Challenge 2.5B anonymized records from 5 months worth of calls made by 5M people at the Ivory Coast to see what it s possible to do with the data" 2013 Telefonica O2 Campus party Footfall counts for London Metropolitan Area over the course of 3 weeks 3 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
4 Mobile Phones regarded as radiolocation bugs" German politician Malte Spitz published his telecom data from 8/09 to 2/10: movements had been captured to 78 %. ZEIT ONLINE enriched" this data set with public and for everybody easily available data records (Tweets and blogs). Swiss politician Balthasar Glättli published his Telecom data from 1/13 8/13. Sources: / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
5 Big Data: Big Concerns Big data technology enables massive data aggregation beyond what has been previously possible Inferencing concerns with non-sensitve data 5 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
6 Safeguards In the field of analytics, customer data can be used responsibly in two ways: the appropriate customer permissions (consent) must be in place, or it must be anonymized so that no individual can be identified. In Europe, anonymized data fall out of the scope of data protection legislation. 1 1 An exception is the French Data Protection Law. 6 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
7 Anonymization of Personal Data Macro data On Mondays on trajectory X there are 160% more passengers than on Tuesdays. Micro data Data is at the granularity of individuals! Micro data Researcher Re-identification risk! 7 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
8 Medical Data Released as Anonymous SSN Name Race DoB Sex ZIP Status Health Problems asian 09/27/64 female divorced hypertension asian 09/30/64 female divorced obesity asian 04/18/64 male married chest pain asian 04/15/64 male married obesity black 03/13/63 male married hypertension black 03/18/63 male married shortness of breath black 09/13/64 female married shortness of breath black 09/07/64 female married obesity white 05/14/61 male single chest pain white 05/08/61 male single obesity white 09/15/61 female widow shortness of breath Voter List Name Address City ZIP DoB Sex Party Sue. J. Carlson 900 Market St. San Francisco /15/61 female democrat 8 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
9 Protecting Microdata Tables Anonymization techniques transform the values of the quasi-identifier attributes. Two main anonymization techniques: randomization and generalization. 09/15/ , or ** k-anonymity Each release of data must be such that every combination of values of quasi-identifiers can be indistinctly matched to at least k customers. Trade-off between Data Utility and Privacy! Additional administrational safeguards such as contractual terms may be needed (limited disclosure). 9 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
10 Data Types and Uniqueness Relational Data Transactional (set-valued) Data Sequential Data Trajectories Graphs Text Credit: Yves-Alexandre de Montjoye, et al. How much personal data you need to know for unique re-identification: (year of birth, sex, 3-digit Zipcode) 0.04% of the American population (date of birth, sex, 5-digit Zipcode) 63 87% of the American population 2 spatio-temporal points 50% 4 spatio-temporal points 95% Re-identification requires outside information! 10 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
11 Anonymization of Trajectory Data ID Trajectory Status... 1 b2 d3 c4 f 6 c7 On-welfare... 2 f 6 c7 e8 Student... 3 d3 c4 f 6 e8 Retired... 4 b2 c5 c7 e8 Student... 5 d3 c7 e8 Retired... 6 c5 f 6 e8 Full-time... 7 b2 f 6 c7 e8 Full-time... 8 b2 c5 f 6 c7 On-welfare... high dimensionality data sparseness sequential LKC-Privacy: Every sequence with maximum length L of any trajectory is shared by at least K records, and the confidence of inferring any sensitive value is not greater than C. 11 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
12 Anonymization of Trajectories An evolving research field Source: Abul, Bonchi & Nanni: Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases, / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
13 Prepare to share but be aware Spatio-temporal data is very unique Increasing public availability of other datasets ( Open Data movement) What is needed: Appropriate anonymization techniques Do anonymization algorithms scale? Additional administrational safeguards such as internal controls and contractual terms Telco operators have to weigh up legal & reputational risk vs. business opportunity. 13 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
14 When disclosing data it does not matter how sensitive the data is for us but how characteristic. It is the latter that determines the effort necessary to link them with other data to uncover our identity. 14 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
15 Thank You for Your Attention Lucerne University of Applied Scienes and Arts Competence Center Information Security Dr. Günter Karjoth Lecturer on Information Security and Privacy Zentralstrasse 9, CH-6002 Luzern T: Credit: Jorge Stolfi 15 / 15 Dr. Günter Karjoth Privacy Challenges of Telco Big Data June 17, Günter Karjoth
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