Data Anonymization - Generalization Algorithms Li Xiong CS573 Data Privacy and Anonymity.
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Transcript of Data Anonymization - Generalization Algorithms Li Xiong, Slawek Goryczka CS573 Data Privacy and...
![Page 1: Data Anonymization - Generalization Algorithms Li Xiong, Slawek Goryczka CS573 Data Privacy and Anonymity.](https://reader036.fdocuments.us/reader036/viewer/2022081801/5a4d1b437f8b9ab0599a1e78/html5/thumbnails/1.jpg)
Data Anonymization -
Generalization Algorithms
Li Xiong, Slawek Goryczka
CS573 Data Privacy and Anonymity
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Generalization and Suppression • Generalization
Replace the value with a less specific but semantically consistent value
# Zip Age Nationality Condition1 41076 < 40 * Heart Disease2 48202 < 40 * Heart Disease3 41076 < 40 * Cancer4 48202 < 40 * Cancer
Suppression Do not release a
value at all
Z0 = {41075, 41076, 41095, 41099}
Z1 = {4107*. 4109*}
Z2 = {410**}
S0 = {Male, Female}
S1 = {Person}
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3
ComplexitySearch Space:
• Number of generalizations = (Max level of generalization for attribute i + 1)
attrib i
If we allow generalization to a different level for each value of an attribute:
• Number of generalizations = (Max level of generalization for attribute i + 1)
attrib i
#tuples
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Hardness result
Given some data set R and a QI Q, does R satisfy k-anonymity over Q? Easy to tell in polynomial time, NP!
Finding an optimal anonymization is not easy NP-hard: reduction from k-dimensional perfect
matching A polynomial solution implies P = NP
A. Meyerson and R. Williams. On the complexity of optimal k-anonymity. In PODS’04.
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Anonymization Strategies
Local suppression Delete individual attribute values e.g. <Age=50, Gender=M, State=CA>
Global attribute generalization Replace specific values with more general
ones for an attribute Numeric data: partitioning of the attribute
domain into intervals, e.g., Age = {[1-10], ..., [91-100]}
Categorical data: generalization hierarchy supplied by users, e.g., Gender = {M, F}
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01/31/12 6
k-Anonymization with Suppression
k-Anonymization with suppression Global attribute
generalization with local suppression of outlier tuples.
Terminologies Dataset: D Anonymization: {a1, …, am} Equivalent classes: E
vn,mv1,n
…v1,m…v1,1
a1 am
E{
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Finding Optimal Anonymization
Optimal anonymization determined by a cost metric
Cost metrics Discernability metric: penalty for non-
suppressed tuples and suppressed tuples
Classification metric
R. Bayardo and R. Agrawal. Data Privacy through Optimal k-Anonymization. (ICDE 2005)
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Modeling Anonymizations
Assume a total order over the set of all attribute domains
Set representation for anonymization e.g., Age: <[10-29], [30-49]>, Gender: <[M or F]>, Marital
Status: <[Married], [Widowed or Divorced], [Never Married]> {1, 2, 4, 6, 7, 9} -> {2, 7, 9}
Power set representation for entire anonymization space Power set of {2, 3, 5, 7, 8, 9} - order of 2n! {} – most general anonymization {2,3,5,7,8,9} – most specific anonymization
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Optimal Anonymization Problem
Goal Find the best anonymization
in the powerset with the lowest cost
Algorithm set enumeration search
through tree expansion - size 2n
Top-down depth first search
Heuristics Cost-based pruning Dynamic tree rearrangement
Set enumeration tree over powerset of {1,2,3,4}
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Node Pruning through Cost Bounding
Intuitive idea prune a node H if none of its
descendents can be optimal Cost lower-bound of
subtree of H Cost of suppressed tuples
bounded by H Cost of non-suppressed
tuples bounded by A
H
A
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Useless Value Pruning
Intuitive idea Prune useless values
that have no hope of improving cost
Useless values Only split
equivalence classes into suppressed equivalence classes (size < k)
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Tree Rearrangement
Intuitive idea Dynamically reorder
tree to increase pruning opportunities
Heuristics sort the values
based on the number of equivalence classes induced
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Comments
Interesting things to think about Domains without hierarchy or total order
restrictions Other cost metrics Global generalization vs. local generalization
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Taxonomy of Generalization Algorithms Top-down specialization vs. bottom-up
generalization Global (single dimensional) vs. local (multi-
dimensional) Complete (optimal) vs. greedy (approximate) Hierarchy-based (user defined) vs. partition-
based (automatic)
K. LeFerve, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient Full-Domain k-Anonymity. In SIGMOD 05
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Generalization algorithms Early systems
µ-Argus, Hundpool, 1996 - Global, bottom-up, greedy Datafly, Sweeney, 1997 - Global, bottom-up, greedy
k-Anonymity algorithms AllMin, Samarati, 2001 - Global, bottom-up, complete, impractical MinGen, Sweeney, 2002 - Global, bottom-up, complete, impractical Bottom-up generalization, Wang, 2004 – Global, bottom-up, greedy TDS (Top-Down Specialization), Fung, 2005 - Global, top-down, greedy K-OPTIMIZE, Bayardo, 2005 – Global, top-down, partition-based, complete Incognito, LeFevre, 2005 – Global, bottom-up, hierarchy-based, complete Mondrian, LeFevre, 2006 – Local, top-down, partition-based, greedy
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Mondrian
Top-down partitioning Greedy Local (multidimensional) – tuple/cell level
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Global Recoding
Mapping domains of quasi-identifiers to generalized or altered values using a single function
Notation Dxi is the domain of attribute Xi in table T
Single Dimensional φi : Dxi D’ for each attribute Xi of the quasi-
id φi applied to values of Xi in tuple of T
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Local Recoding
Multi-Dimensional Recode domain of value vectors from a set of
quasi-identifier attributes φ : Dx1 x … x Dxn D’ φ applied to vector of quasi-identifier attributes
in each tuple in T
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Partitioning Single Dimensional
For each Xi, define non-overlapping single dimensional intervals that covers Dxi
Use φi to map x ε Dx to a summary stat Strict Multi-Dimensional
Define non-overlapping multi-dimensional intervals that covers Dx1… Dxd
Use φ to map (xx1…xxd) ε Dx1…Dxd to a summary stat for its region
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Global Recoding Example
Multi-Dimensional
Single Dimensional
Partitions Age : {[25-28]} Sex: {Male, Female} Zip : {[53710-53711], 53712}
Partitions {Age: [25-26],Sex: Male, Zip: 53711} {Age: [25-27],Sex: Female, Zip: 53712} {Age: [27-28],Sex: Male, Zip: [53710-53711]}
k = 2Quasi Identifiers
Age, Sex, Zipcode
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Global Recoding Example 2
k = 2Quasi Identifiers
Age, Zipcode
Patient Data Single Dimensional Multi-Dimensional
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Greedy Partitioning Algorithm
Problem Need an algorithm to find multi-dimensional
partitions Optimal k-anonymous strict multi-dimensional
partitioning is NP-hard Solution
Use a greedy algorithm Based on k-d trees Complexity O(n logn)
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Greedy Partitioning Algorithm
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Algorithm Example
k = 2 Dimension determined heuristically Quasi-identifiers
Zipcode Age
Patient Data Anonymized Data
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Algorithm ExampleIteration # 1 (full table)partition
dim = Zipcode
splitVal = 53711
`
LHS RHS
fs
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Algorithm Example continuedIteration # 2 (LHS from iteration # 1)
partitiondim = Age
splitVal = 26LHS RHS
fs
`
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Algorithm Example continuedIteration # 3 (LHS from iteration # 2)
partitionNo Allowable Cut`
Summary: Age = [25-26] Zip= [53711]
Iteration # 4 (RHS from iteration # 2)partition
No Allowable Cut`
Summary: Age = [27-28] Zip= [53710 - 53711]
`
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Algorithm Example continuedIteration # 5 (RHS from iteration # 1)
partitionNo Allowable Cut`
Summary: Age = [25-27] Zip= [53712]
`
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Experiment
Adult dataset Data quality metric (cost metric)
Discernability Metric (CDM) CDM = ΣEquivalentClasses E |E|2
Assign a penalty to each tuple Normalized Avg. Eqiv. Class Size Metric (CAVG)
CAVG = (total_records/total_equiv_classes)/k
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Comparison results Full-domain method: Incognito Single-dimensional method: K-OPTIMIZE
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Data partitioning comparison
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Mondrian
Piet Mondrian [1872-1944]
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Distributed Anonymization
aggregate-and-anonymizeanonymize-and-aggregate
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Anonymization Example (attack)
Privacy is defined as k-anonymity (k = 2).
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Anonymization Example (attack)
Privacy is defined as k-anonymity (k = 2).
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Anonymization Example (attack)
Privacy is defined as k-anonymity (k = 2).
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m-Privacy
A set of anonymized records is m-private with respect to a privacy constraint C, e.g., k-anonymity, if any coalition of m parties (m-adversary) is not able to breach privacy of remaining records.
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m-Anonymization Example
An attacker is a single data provider (1-privacy)
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Parameters m and C Number of malicious parties: m
m = 0 (0-privacy) is when the coalition of parties is empty, but each data recipient can be malicious
m = n-1 means that no party trusts any other (anonymize-and-aggregate)
Privacy constraint C: m-privacy is orthogonal to C and inherits all its
advantages and drawbacks
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m-Adversary Modeling
If a coalition of attackers cannot breach privacy of records, then any its subcoalition will not be able to do so as well.
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Equivalence Group Monotonicity Adding new records to a private equiv. group will
not change the privacy fulfillment! To verify m-privacy it is enough to determine
privacy fulfillment only for m-adversaries, EG monotonic privacy constraints: k-anonymity,
simple l-diversity, … Not EG monotonic constraints:
t-closeness, ...
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Pruning Strategies
Number of coalitions to verify: exponential to number of providers, but with efficient pruning strategies should be OK!
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Verification Algorithms
top-down algorithm,bottom-up algorithm, binary algorithm.
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Anonymizer for m-Privacy
To multidimensional data add one more attribute – data provider, which can be used as any other attribute in anonymization.
ZipZip
Age
Provider
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Anonymizer for m-Privacy
To multidimensional data add one more attribute – data provider, which can be used as any other attribute in anonymization.
Zip
Age
Provider
Age
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Anonymizer for m-Privacy
To multidimensional data add one more attribute – data provider, which can be used as any other attribute in anonymization.
Zip
Age
Provider Provider
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m-Anonymizer (diagram)
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Experiments Setup Dataset: the Adult dataset, Census database. Attributes: age, workclass, education, marital-
status, race, gender, native-country, occupation (sensitive attribute with 14 possible values).
Privacy defined as a conjunction of k-anonymity and l-diversity.
Metrics: Runtime Query error – compares results of random
queries issued over original and anonymized data
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Experiments
m-Privacy verification runtime for different algorithms vs. m
Average number of records per provider = 10 Average number of records per provider = 50
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Experiments
m-Anonymizer runtime and query error for different anonymizers vs. size of attacking coalitions m
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Experiments
m-Anonymizer runtime and query error for different anonymizers vs. number of data records
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Q & A
Thank you!