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![Page 1: Holistic Privacy From Location Privacy to Genomic Privacy Jean-Pierre Hubaux With contributions from E. Ayday, M. Humbert, J.-Y. Le Boudec, J.-L. Raisaro,](https://reader036.fdocuments.in/reader036/viewer/2022062720/56649efa5503460f94c0d154/html5/thumbnails/1.jpg)
Holistic PrivacyFrom Location Privacy to Genomic Privacy
Jean-Pierre HubauxWith contributions from
E. Ayday, M. Humbert, J.-Y. Le Boudec, J.-L. Raisaro, R. Shokri, G. Theodorakopoulos
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Make It Faster!!
2
Benz Motorwagen, 1885
Ford-T, 1915
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After Some Decades…
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… the Concerns Have Changed
• Reduce casualties– Better brakes– Safety belts– Airbags– …
• Mitigate side effects– Road congestion– Depletion of fossil fuel– Climate change– ….
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Similar Phenomenon with IT
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For each end user:
•10s to 1000s Mb/s•Terabytes of storage•Processor in the Ghz
Assault on privacy
Cyber-crime, cyberwar
Information overload,attention deficit disorder
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Holistic PrivacyFrom Location Privacy to Genomic Privacy
1. On Privacy Protection2. Location Privacy3. Genomic Privacy
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Another Observation Tool…
“The Right to Privacy”Warren and BrandeisHarvard Law Review
Vol. IV Dec. 15, 1890 No. 5 7
Major concern: photography without consent
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Some Modern Observation Tools
8Cellularphones Online Social Networks
Genomicsequencing
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Privacy: Definition
• Privacy control is the ability of individuals to determine when, how, and to what extent information about themselves is revealed to others.
• Goal: let personal data be used only in the context they have been released
• Privacy is about the data of individuals
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Main Risk: People’s Mind Manipulation
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Citizens (us)
Those observing us
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Privacy Protection at Odds with…
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Privacy Protection
Security (e.g., homeland security)
Business (e.g., targeted advertisement)
Usability
System performance
Medical progress
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Holistic PrivacyFrom Location Privacy to Genomic Privacy
1. On Privacy Protection2. Location Privacy3. Genomic Privacy
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Users upload location episodically
through WiFi or cellular networks
Query, Location, TimeQuery, Location, Time
Location-Based Services
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Why Reveal Your Location?
• To use service– Cellular connectivity– Location-based services– Local recommendations– Road toll payment– …
• For social benefits– Find friends
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Can You Clean up Your Digital Trace?
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0203
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10111213 14
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0102
0304
05060708
091011
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13 1415
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events-----------------------------------------------
Color: user identityNumber: time-stampPosition on the map: location-stamp
0101
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Threat
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The contextual information attached to a trace tells much about our habits, interests, activities, beliefs and relationships
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Quantification of Location Privacy
• Many privacy-preserving mechanisms proposed
• No unified formal framework in previous work• Various metrics for location privacy
• How to compare different mechanisms?• Which metric to use?
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Time and Space
• Consider discrete time and space
• Attacker: service provider (``honest but curious´´)
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Quantifying Location Privacy
KC: Knowledge Constructor LPPM: Location Privacy Protection Mechanism:-deliberately imprecise coordinate reports (e.g., drop some of the least significant bits)-Swap user identifiers
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Correctness
The adversary’s estimation of x given the observed traces o
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Location-Privacy Preserving Mechanisms
Implemented LPPMs:
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Location-Privacy Meter
Open source software tool (C++) to quantify location privacy
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Location-Privacy Meter (LPM)– Some traces to learn the users’ mobility profiles
(background knowledge)– Observed traces
– Location privacy of users with respect to various attacks: Localization, Tracking, Meeting Disclosure, Aggregate Presence Disclosure,…
LPM
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LPM: Example
• N = 20 users• R = 40 regions• T = 96 time instants
• Protection mechanism:– Hiding location– Precision reduction (dropping
low-order bits from the x, y coordinates of the location)
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Attacks
•LO-ATT: Localization Attack: For a given user u and time t, what is the location of u at t?
•MD-ATT: Meeting Disclosure Attack: For a given pair of users u and v, what is the expected number of meetings between u and v?
•AP-ATT: Aggregated Presence Attack: For a given region r and time t, what is the expected number of users present in r at t?
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Results
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Protecting Location Privacy:Optimal Strategy against Localization Attacks
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Adversary Knowledge:User’s “Location Access Profile”
29Data source: Location traces collected by Nokia Lausanne (Lausanne Data Collection Campaign)
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Location Obfuscation Mechanism
Consequence: “Service Quality Loss”
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Location Inference Attack
Estimation Error: “Location Privacy”
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Problem Statement
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Zero-sum Bayesian Stackelberg Game
User Adversary (leader) (follower)
Game
LBS message
user gain / adversary loss
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Optimal Strategy for the User
Proper probability distribution
Respect service qualityconstraint
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Optimal Strategy for the Adversary
Note: This is the dual of the previous optimization problem
Proper probability distribution
Shadow price of the service quality constraint .(exchange rate between service quality and privacy)
Minimizing the user’s maximum privacy under the service qualityconstraint
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Evaluation: Obfuscation Function
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Output Visualization of Obfuscation Mechanisms
Optimal Obfuscation Basic Obfuscation(k = 7)
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Conclusion on Location Privacy• Protecting location privacy is a major challenge• Quantification expressed as adversary’s expected estimation error
(incorrectness)• Techniques to protect location privacy: introduce imprecision in the
reported location, reduce location report frequency, make use of pseudonyms,…
• Privacy (similarly to any security property) is adversary-dependent. Neglecting adversary’s strategy and knowledge limits the privacy protection
• More information and pointers:http://lca.epfl.ch/projects/quantifyingprivacy
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Holistic PrivacyFrom Location Privacy to Genomic Privacy
1. On Privacy Protection2. Location Privacy3. Genomic Privacy
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On Convergence…
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``The last inch´´
Digital medicine:- Digital medical records- Digital imaging-Medical online social networks-Genome sequencing-Other ´omics data- Wireless biosensors…
Telecom Computing
ICT
…0100110100011… …CGTTAATTCCGTA…
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The Genomic Avalanche Is Coming…
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Genetic Sequencing
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GATTACA (1997 Movie)
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Basics of Genomics – 1• A full genome sequence:
– uniquely identifies each one of us
– contains information about our ethnic heritage, disease predispositions, and many other phenotypic traits.
• Human genome: 3 billion letters
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Basics of Genomics - 2
• The cell’s nucleus holds the genetic program that determines most of our physical characteristics.
• This information is stored in chromosomes.• Billions of identical copies of the genetic program, one for each cell
nucleus.
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Basics of Genomics – 3
• Chromosomes: molecules of a double-stranded chemical known as Deoxyribonucleic acid (DNA)
• DNA consists of chemical units that hook together known as nucleotides
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Basics of Genomics – 4
• DNA has two strands and four nucleotides (A T G C):
• A = Adenosine• T = Thymidine• G = Guanosine• C = Cytidine
• The genetic information is stored in the exact sequence of nucleotides.Pairs: A-T and G-C
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Basics of Genomics – 5
Human Genome complete and ordered sequence of all 23 chromosomes
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Basics of Genomics - 6
• Human Genome identical in most places for all people.
• SNP (Single Nucleotide Polymorphism) positions where some people have one nucleotide pair while others have another.
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Basics of Genomics – 7
• SNPs make up only 1.3% of the genome
• The differences at these places make each of us unique
Allele designates which nucleotide is present at a SNP.
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40 million SNPs
… … ……
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Summary of Key Concepts
• Our genetic information is stored in the sequence of DNA in our chromosomes.
• There are 23 chromosomes in a human genome. Men and women have slightly different sets of chromosomes.
• SNPs are chromosome addresses. They are spots where some people have one nucleotide, while others have another.
• SNPs have four possible alleles: A, T, G, and C.• Our collection of SNP alleles is what makes each of us unique.• Modern techniques make it possible to determine the status
of large numbers of SNPs very efficiently.
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From the Sample to the Full Genome Sequence
Raw data(FASTq)
Full genome
• Individual diagnosis,personalized medicine
• Statistics
Deep / ultra-deep sequencing
SAM file (aligned reads)
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Samples Sequencing machine (Illumina,
Roche, Life Technology,
Oxford Nanopore,PacBioScience,…)
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Threat
• Leakage of genomic data• Revelation of privacy-sensitive data about the
patient – Predisposition to disease, ethnicity, paternity or
filiation, etc.– Denial of access to health insurance, mortgage,
education, and employment• Cross-layer attacks
– Using privacy-sensitive information belonging to a victim retrieved from different sources
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Goals
• Allow specialists to access only to the genomic data they need
• Protect data, including from insiders (e.g., curious sysadmins) homomorphic encryption
• Access time to a single patient’s genomic data below a few seconds
• Access time to the data of a cohort of thousands of patients below a few minutes
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Cryptographic Tools
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Possible Solution
6) M
ark
ers re
late
d to
dise
ase
X a
nd th
eir
contrib
utio
ns
5) “Check my susceptibility to disease X”and part of P’s secret key, x(2)
3) Encrypted variants
8) E
nd-re
sult o
r re
late
d v
aria
nts
7) Homomorphic operationsand proxy encryption
Patient (P)Medical Center
(MC)
1) S
am
ple
Certified Institution Curious Party@ SPU
Malicious 3rd party
Storage and Processing Unit(SPU)
2) Sequencing and encryption
4) Part
of P’s
secre
t key, x
(1)
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Probabilities:
. . .
Markers for disease X:
P’s SNPs:
Contributions
of markers:
P’s susceptibility for disease X:
. . . . . . . . .
Disease Susceptibility – Weighted Averaging
• All operations are conducted in ciphertext using homomorphic encryption. 57
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Prototype – Patient Interface
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Prototype – SPU Interface
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Prototype – Medical Center Interface
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Holistic Privacy: Data about an Individual
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Genome
Human Relationships
Mobility+ Body Area Network
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Conclusion on Genomic Privacy• Digital medicine is coming• It will for ever change the landscape of privacy
protection• Genomics is particularly relevant and there is a huge
ongoing research effort• Highly sensitive data + huge amounts of data + complex
correlations between data Complex field, Big Data• Tools (cryptography, security protocols,
database/differential privacy, anonymization techniques,…) already used for privacy protection in ICT can (and should) be applied here
• More information and pointers: http://lca.epfl.ch/projects/genomic-privacy/
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Overall Conclusion• Assault on privacy huge research challenges • Location privacy
– quantifiable at the physical level ( (x, y) coordinates)– ongoing work at the semantic level
• Online Social Networks part of the background knowledge of the adversary
• Genomic privacy – still in its infancy– soon to be very hot – first results coming out
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