Download Privacy-Aware Computing

Overview of Privacy
Preserving Techniques
 This is a high-level summary of the
state-of-the-art privacy preserving
techniques and research areas
 Focus on problems and the basic
ideas
Outline
 Privacy problem in computing
 Major techniques
 Data perturbation
 Data anonymization
 Cryptographic methods
 Privacy in different application areas
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Data mining
Data publishing
Databases
Data outsourcing
Social network
Mobile computing
Privacy vs. Security
 Network security
 Assumption: the two parties trust each
other, but the communication network is
not trusted.
Alice
Communication channel
Bob
Encrypting
data
Decrypting
data
Bob knows the original data that Alice owns.
 Privacy problems
 Information about a person or a single party
 Parties do not trust each other: curious parties
(including malicious insiders) may look at
sensitive contents
 Parties follow protocols honestly (semi-honest
assumption)
Alice
Deliver “sanitized” data
Bob
Bob is an untrusted party.
He may try to figure out some
Private information from the
sanitized data
Two categories
(1) Transformation based methods
a “curious party”
Alice
Communication channel
Bob
transformed
data
Works on the transformed
data only
Bob does not know the original data.
(2) Cryptographic protocol methods
Some protocol using
cryptographic primitives
Statistical Info/
Intermediate
result
Info from other
parties
Party 1
Party 2
Party n
data
data
data
Computing scenarios
 Web model
user 1
user 1
Private
info
Web
Apps
data
user 1
 collaboration model
Party 1
Party 2
Party n
data
data
data
 Outsourcing model
Data
owner
data
Export
Service
data
provider
to use
the service
Issues with data transformation
 Techniques performing the transformation
 Transformation should preserve important information
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How much information loss
How to recover the information from the transformed data
 Threat model
 Attacks reconstructing the original data from the
transformed data
 Attacks finding significant additional information
 The cost
 Transforming data
 Recovering the important information
Transformation techniques
 Data Perturbation
 Additive perturbation
 Multiplicative perturbation
 Randomized responses
 Data Anonymization
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k-anonymization
l-diversity
t-closeness
m-invariance
Attacks on transformation techniques
 Data reconstruction and noise
reduction techniques (on data
perturbation)
 random matrix theory
 spectral analysis
 Inference attacks (on data
anonymization)
 Utilizing background knowledge
Cryptographic approaches
Using the following cryptographic primitives
 Secure multiparty computation (SMC)
 Yao’s millionaire problem
 Alice wants to know whether she has more money than
Bob
 Alice&Bob cannot know the exact number of each
other’s money. Alice knows only the result
 Oblivious transfer
 Bob holds n items. Alice wants to know i-th item.
 Bob cannot know i – Alice’s privacy
 Alice knows nothing except the i-th item
 Homomorphic encryption
 Allow computation on encrypted data
 E.g., E(X)*E(Y) = E(X+Y)
 Characteristics:
 Pro: preserving total privacy
 Con: expensive, limited # of parties
 Applications: for distributed datasets
(the corporate model)
 Protocols for data mining algorithms
 Statistical analysis (matrix, vector
computation)
 Often discussed in two-party (or a small
number of parties) scenarios.
Privacy-preserving data mining
 Purpose
 Mining the models without leaking the
information about individual records
 topics
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Basic statistics (mean, variance, etc.)
Data classification
Data clustering
Association rule mining
Privacy of mined models
Privacy preserving database
applications [Du&Atallah2000]
Statistical databases
Private information retrieval
Outsourced databases
Social Network Privacy
 Publishing social network structure
Anonymization is a popular method
 Attacks can be applied to reveal the
mapping [163,167]
 Characteristics of subgraph
 Adversarial background knowledge
Social network privacy
 Privacy settings of SN
 Help users set/tune privacy settings
 Understand the relationship between
privacy and functionalities of SN
 They are a pair of conflicting factors
Privacy in Mobile computing
 Preserving location privacy
 User-defined or system supplied privacy
policies [Bamba&Liu2008, Beresford&Stajano2003]
 Extending k-anonymity techniques to
location cloaking [Gedik&Liu2008,
Gruteser&Grunwald2002]
 Pseudonymity of user identities –
frequently changing internal id.
[Beresford&Stajano2003]