Privacy-preserving of Trajectory Data : A Survey

Huo Zheng

OUTLINE•Motivating Applications•Privacy-preserving in Different

Scenarios•Conclusions & Future work

Motivating Applications

1. Trajectory

data publicatio

n & analysis

2.LBS

4.Trajectory

data outsourci

ng

3.ITS

OUTLINE•Motivating Applications•Privacy-preserving in Different

Scenarios•Conclusions & Future work

Solutions-overviewData

PublicationData

Outsourcing ITS LBS

Suppression[Terrovitis MDM’08]

[Abul ICDMW’07]

[Gruteser ISE’04]

Anonimization

[Ghinita TDP’09][Nergiz TDP’09][Abul ICDE’08]

[Divanis SIAM’09]

[Hoh MobiSys’08]

[Xu INFOCOM’08][Gidofalvi MDM’07]

Perturbation[Hoh

SecureCom’05][ You PALSM’07]

[Lee CIKM’09]

Encryption [Xu Proposal’10]

Scenario #1

Trajectory data

publication & analysis

Trajectory data

outsourcing

LBSITS

Solutions #1 Overview• Protecting trajectory data privacy against attackers in the

following aspects:

▫ Protecting trajectory data to be identified by the adversary

▫ Protecting sensitive location samples in trajectory data.

▫ Attackers may have background knowledge to induce users’ information，For example, home and work place can help adversary to infer the trajectory’s owner

▫ Protect data privacy while preserving the utility of dataData

Privacy

Data Utility

Dummies• Basic Idea

▫Increasing the number of possible trajectories from the adversaries’ perspective

▫Decreasing disclosure of the user trajectory

• Method▫Generate dummy trajectories as

human behavior

▫Generate dummy trajectories with distances larger than a predefined distance deviation

[You PALMS’07]

Dummies (cont’)•Procedure

▫Set a disclosure rate▫Generate dummies

Random Trajectories with

intersections Rotate

Compute distance deviation

[You PALMS’07]

Source

destination

Pros and cons• Pros

▫Attackers can’t distinguish which trajectory is real user trajectory under a threshold which is given by users

▫Simple, easy to understand• Cons

▫High cost in storage, for example, to protect a single trajectory, you need to store several dummy trajectories, causing lower data utility.

▫High disclosure rate for adversaries with strong background knowledge

[You PALMS’07]

Suppress locations in trajectory data publication

• Basic Idea▫ Suppress location samples in a

trajectory database

• Procedure▫ Decide which location to

suppress

If the location sample is sensitive, suppress it.

If the location sample may reveal other information, suppress it.

▫ Suppress the location when publishing data

[Terrovitis MDM’08]

Id Loc1 Loc2 Loc3 Loc4 Loc501 (1, 3) (1, 5) (2, 6) (2,9) (3,10)

02 (2, 5) (4, 8) (5, 10) (5,15) (5,20)

03 (0,2) (4, 2) (5, 4) (5,10) (6,11)

04 (2, 3) (2, 5) (2, 8) (3,9) (3,15)

Loc Name

(2,6) Clinic

(5,20) Hotel

(3,15) Bar

Privacy preservation in the publication of trajectories

•Motivation▫Octopus RFID card is

commonly used by HK residents to pay for their transportations, transactions at point-of-sale services;

▫If the Octopus company publish the data directly, it may cause privacy linkage, since other agencies may have partial knowledge of a same person.

ID Trajectory

t1 a1->a3

a1

a3

[Terrovitis MDM’08]

An Example

[Terrovitis MDM’08]

Suppress

Compute distortion

Pros and Cons•Pros

▫Protecting moving objects’ privacy even the adversaries have partial knowledge

▫Easy to understand, low computation cost.

•Cons▫May cause serious information loss if

suppressed too much location samples.

Never Walk Alone• Motivation

▫Due to the imprecision of GPS devices, where its radius δ represents the possible location imprecision

• Key Idea▫Anonymize trajectories in a same time span under

uncertainty δ

[Abul ICDE’08]

Never Walk Alone(cont’)•Key Methods

▫Preprocessing Uniform

trajectories in a same time span

▫Clustering Greedy Clustering

based on the Euclid distance

▫(K, δ)-anonymity Space translation

t1 tn

…… ……

…………

Time

x

y

[Abul ICDE’08]

Pros and cons• Pros

▫It exploits the inherent uncertainty of location in order to reduce the amount of distortion needed to anonymize data;

▫It is a simple, efficient and effective method.

• Cons▫It assumes a uniform uncertainty level, in some applications

it is not suitable;

▫Due to the limitation of the uncertainty level, distortion grows rapidly when K is larger.

Towards trajectory anonymity•Motivation

▫To improve the utility of the published data Most data mining and statistical applications

work on atomic trajectory

•Procedure▫Trajectory grouping

Logic cost metric▫K-Anonymity▫Reconstruction

[Nergiz TDP’09]

An Example

[Nergiz TDP’09]

Anonymization tr* of tr1 and

tr2

Anonymization tr* and tr3

Randomly select points

Reconstruction

Complete

Conclusions•Trajectory data privacy preserving in data

publication has been widely studied.

•Several methods are proposed in trajectory data privacy preserving, most of them come from privacy preserving in data publication.

•Challenges lies in privacy preserving in high frequency sampling while providing high quality of data utility.

Scenario #2

Trajectory data

mining LBS

Trajectory data

outsourcing

ITS

Solutions #2 overview•Protecting trajectory data privacy against

attackers in the following aspects▫Protecting trajectory privacy against non-

trustworthy LBS server

▫Protecting users’ privacy when acquiring LBS services, such as sending queries.

▫Protecting data privacy while providing high quality of services. MOB’

privacy

QoS

Navigational path privacy protection

•Motivation▫Navigational path query

is one of the most popular LBS, which determines a route from a source to a destination

▫Issuing path queries to some non-trustworthy service providers may pose privacy threats

[Lee CIKM’09]

User queries

Mr.Q is going to a psychiatrist , he may have

some psychopathic

ward

Service providers

Queries

How to get to the

psychiatrist from home?

Results

Navigational path privacy protection(cont’)

• Solutions▫ Landmark: replace both

source and destination of a path query Q(s, t) to with other locations, thus resulting in another path query Q(s’, t’)

▫ Cloaking: it may cloak both the source and destination into locations at the same street level, the result may be irrelevant.

[Lee CIKM’09]

Navigational path privacy protection(cont’)

•Solutions▫Obfuscate a path query by

injecting some fake sources and destinations

•Three methods▫Independent obfuscate path

query▫Shared obfuscate path query▫Anti-collusion path query

s

t

Mr.Q ’s home

Clinic

S

T

[Lee CIKM’09]

System overviewIndependent obfuscate query : Obfuscate one independent path queries by randomly inject fake locationsS={sA, s1}, T={tA, t1, t2}Pb=1/2*3=1/6

Shared obfuscate query: Obfuscate two or more path together with injecting fake locations.S={sA, s1, sB}, T={tA, t1, t2, tB}Pb=1/3*4=1/12

Anti-collusion obfuscate query: Injecting more fake locations in order to get a low breach probability.S={sA, s1, s2, sB}, T={tA, t1, t2 t2, tB} Pbmin=1/4*5=1/20; Pbmax=1/2*3=1/6

[Lee CIKM’09]

Pros and Cons•Pros

▫Developed a framework to obfuscate path queries in order to protect mobile users’ trajectory privacy

▫Mixing some fake sources and destinations greatly reduced the breach probability

•Cons▫Provide weak privacy protection when the

adversary have strong background knowledge

Cut-Enclose•Motivation

▫Overlapping of trajectory anonymity rectangles may cause location privacy linkage

▫Simply cut and enclose methods may cause privacy leakage in the joint of grids

[Gidofalvi MDM’07]

Problems with existing methods

Time delay factor

[ti-1,ti][ti,ti+1] [ti+1,ti+2]

Problems with simple cut-enclose

Cut-Enclose(cont’)

Common Regular Partitioning

Anonymized trajectory

[Gidofalvi MDM’07]

Individual Regular Partitioning

Individual Irregular Partitioning

• Procedure▫ Users set privacy levels

(individual privacy level/region sensitive level);

▫ Separate 2D space into grids;

▫ According to user specified individual privacy level (CRP /IRP)or region sensitive level(IIR), combine girds into partitions;

▫ Anonymize trajectory pieces in each partition with time delay factor.

Anonymity with historical data•Motivation

▫Existing cloaking methods highly depend on the network density ;

▫Existing methods are not suitable for time-series sequence The cloaking box form a

trajectory that may disclose a user’s trajectory.

[Toby INFOCOM’08]

?

Anonymity with historical data(cont’)

a1a2

a3

a4 a

5a6

a7

a8

c1c2

c3c4

C1C2

C3C4

[Toby INFOCOM’08]

•ProcedureClocking one additive trajectory1. Select a pivot for each footprint;2. Choose the one with the smallest

MBC and index No. as the next pivot;3. Until all trajectory points of the base

trajectory is all anonymized.

Cloaking K-1 additive trajectory1. Liner: the cloaking result is

considered as a new base trajectory T0

2. Quadratic: the selection of the new trajectory is based on its distance to T, not T0T0

Ta

b1b2

b3

b4 b

5b6

b7

Tb

Trajectory data

miningLBS

Trajectory data

outsourcing

ITS

Senario #3

Privacy preserving traffic monitoring•Motivation

▫GPS-equipped vehicles send their location info to traffic monitoring center in a regular frequency

▫The location traces might reveal sensitive places that drivers have visited

[Hoh MobiSys’08]

Privacy preserving traffic monitoring(cont’)

•Key Idea▫Minimizing tracking

time reduces the risk that an adversary can correlate an identity with sensitive locations

•Method▫A time-to-confusion

level▫An uncertainty level

[Hoh MobiSys’08]

Conclusions•Trajectory data privacy preserving in

online applications are necessary, no dominant methods exists to solve this problem.

•Challenges lies in the current trajectory privacy preserving without location privacy leakage while providing high quality of online services.

Scenario #4

Trajectory data

miningLBS

Trajectory data

outsourcing

ITS

Solutions #2 overview

•Motivation▫Cloud emerges as a new way of DaaS;

▫More and more agencies are moving their data to the cloud, they worried the privacy and security in the cloud;

▫Privacy protection in the cloud is necessary.

Dark Cloud

Green Cloud

[Xu Proposal’10]

Privacy Threats in the Cloud•Users’ Query Privacy

▫Eg. Mr.Q want to protect his query against the Cloud, since his query is about mental disease

•Data Privacy of the Data Owner

•Mutual Privacy ▫Semi-honest model

Cloud

Data Owner

Data

Query

Results

[Xu Proposal’10]

Main FrameworkData Owner encrypts the database R and sends it to the Cloud

Data Owner sends a shadow index E(I) and S-1() to the client, and sends E-1() to the Cloud for the following processing

E(i) is retrieved locally and encrypts as Ec(E(i)), then sent back to the Cloud for decryption

The Cloud decrypted Ec(E(i)) to get Ec(i), return it to the client.

If it is a leaf node, decrypt it with S-1(), get the result. If it is not a leaf node, get the next i

[Xu Proposal’10]

Research issue• Efficient Privacy-Preserving Query

Processing Techniques▫ Challenges lie in those complex

queries, especially queries that are based on distances. Typical examples like k-nearest neighbor (kNN)

• Privacy-Aware Query Result Authentication Techniques▫ If the cloud is malicious or does not

follow the protocol faithfully, there is a need for the client to authenticate the correctness of query results

Cloud

“Nearest Clinic”Results

[Xu Proposal’10]

OUTLINE•Motivating Applications•Privacy-preserving in different

scenarios•Conclusions & Future work

CONCLUSIONS• This survey discussed trajectory data privacy

preservation techniques

▫For online trajectory data privacy preservation, service is centric, trade-off is between QoS and privacy preservation

▫For offline trajectory data privacy preservation, data is centric, trade-off is between data quality and privacy preservation

• Most of the techniques deals with this problem in free space, and most of them are offline algorithms

FUTURE WORK○ Complete the survey in following aspects:

1. Privacy preserving in time-series data.2. Privacy preserving in outsourcing data.3. ……

○ Trajectory data protection in online applications

● Trajectory data protection in data publication / data outsourcing1. ITS/LBS2. Trajectory data outsourcing

References• G.Gidofalvi, X. Huang, and T. B. Pedersen. Privacy-Preserving Data Mining on Moving Object

Trajectories, In proceedings of MDM’07, 2007• J. Krumm. Inference attacks on location tracks. In Proceedings of the 5th International Conference

on Pervasive Computing (Pervasive 2007), May 2007.• M. Terrovitis, and N. Mamoulis. Privacy Preserving in the Publication of Trajectories. In

proceedings of MDM’08, 2008• A.Gkoulalas-Divanis, V.S.Verykios. A Privacy-Aware Trajectory Tracking Query Engine. In

proceedings of SIGKDD 2008.• Mehmet Eran Nergiz, Maurizio Atzori, Yucel Saygin, Baris Guc. Towards Trajectory Anonymization:

a Generalization-Based Approach. IEEE Transactions on Data Privacy 2(2009) 47-75.• Tun-Hao You, Wen-Chih Peng, Wang-Chien Lee. Protecting Moving Trajectories with Dummies. In

proceedings of PALMS 2007.• Kido H., Yanagisawa Y., Satoh T..An anonymous communication technique using dummies for

location based services. In proceedings of ICPS 2005 • O. Abul, F. Bonchi, and M. Nanni. Never Walk Alone: Uncertainty for Anonymity in Moving Objects

Databases. In proceeding of ICDE 2008.• G.Ghinita. Private Queries and Trajectory Anonymization: a Dual Perspective on Location Privacy.

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Transformation and Encryption. In proceedings of SIGMOD ’10, 2010.• T. Xu, Y. Cai. Exploring Historical Location Data for Anonymity Preservation in Location-based

Services. In Proceedings of INFOCOM’08, 2008.• K. C. K. Lee, W. Lee, H.Va Leong, B.Zheng. Navigational Path Privacy Protection. In Proceedings of

CIKM’09 2009.

References(cont’)• A. Gkoulalas-Divanis, V.S. Verykios, M. F. Mokbel. Identifying Unsafe Routes for

Network-Based Trajectory Privacy. In Proceedings of SPC’09. 2009• O. Abul, M. Atzori, F. Bonchi, F. Giannotti. Hiding Sensitive Trajectory Patterns. In

Proceedings of ICDMW’07, 2007.• M. Gruteser, X. Liu. Protecting Privacy in Continuous Location-Tracking

Applications. In IEEE Security and Privacy, 2004.• X. Pan, X. Meng, J.Xu. Distortion-based Anonymity for Continuous Queries in

Location-Based Mobile Services. In Proceedings of SIGGIS’09, 2009.• S.Mukherjee , Z. Chen, A. Gangopadhyay. A privacy-preserving technique for

Euclidean distance-based mining algorithms using Fourier-related transforms. InVLDB Journal (2006) 15:293–315

• B. Hoh, M. Gruteser, H.Xiong, A. Alrabady. Preserving Privacy in GPS Traces via Uncertainty-Aware Path Cloaking. In proceedings of CCS’07, 2007

Q&A

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