Memorization Property

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Memorization Property

Definition Single-Issuer Historical Attacks

Query Tracking Attack Maximum Movement Boundary Attack

Multiple-Issuers Historical Attacks Notion of Historical k-Anonymity

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Memorization PropertyDefinition

k-anonymity property: the spatial cloaking algorithm generates a cloaked area that cover k different users, including the real issuer.

PrivacyMiddleware

r

D E

B

AC

r’

Service Provider

Cloaked area contains k users

IssuerA

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k users in the cloaked area are easy to move to different places.Attacker which knowledge of exact location of users, has chance to infer the real issuer from the anonymity set.

D E

B

AC RISK !

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memorization property: the spatial cloaking algorithm memorizes the movement history of each user and utilize this information when building cloaked area.

D E

B

AC

Spatial Cloaking Algorithm Processor

movement patterns

cloaked region

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Lacking of memorization property the issuer may suffer from the following attacks:

Single-Issuer Historical Attacks: attacker consumes historical movement of single issuer Query Tracking Attack Maximum Movement Boundary Attack

Multiple-Issuers Historical Attacks: attacker use multiple users historical movement Notion of Historical k-Anonymity

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Memorization PropertyQuery Tracking Attack

Case description: User query is requested

multiple times at ti, ti+1, etc.

Attacker knows exact location of each user.

Attack description: Attacker reveal real issuer by

intersecting the candidate-sets between the query instances

D E

BI

J

A

F

H

K

G

C

At time ti {A,B,C,D,E}

At time ti+1{A,B,F,G,H}

At time ti+2 {A,F,G,H,I}Reveal A

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Memorization PropertyQuery Tracking Attack

Possible instant solution: Delay request until the cloaked until most of the candidate return

Make new cloaked area, consuming users location history. Etc.

D E

BIA

F

HG

C

At time ti

D

EBIA

F

HG

CAt time ti +k

Risky Delay

D

E

BIAFH

G

C

At time ti+k+m

Safe Forward

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Memorization PropertyMaximum Movement Boundary Attack

Case description: Consider the movement rate

(speed) of users. Attacker knows exact

location and speed of each user.

Attack description: Attacker limit the real issuer

into the overlap area

Ri

Ri+1

I know you are here!

movement bound area

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Solution must satisfy one of the three cases:

Ri

Ri+1

① The overlapping area satisfies user requirements

Ri

Ri+1

② Ri totally covers Ri+1

Ri

Ri+1

③ The MBB of Ri totally covers Ri+1

9 Possible solutions are Patching and Delaying

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Patching: Combine the current cloaked spatial region with the previous one

Delaying: Postpone the update until the MMB covers the current cloaked spatial region

Ri

Ri+1

Ri

Ri+1

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Memorization PropertyHistorical k-Anonymity

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If attacker also considers users frequent movement patterns, he has more chance to differ the real issuer with other candidates.

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Memorization PropertyHistorical k-Anonymity Terminology

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Quasi-identifier (QID): set of attributes which can be used to identify an individual.

Location-Based QIDs (LBQIDs): Spatio-temporal movement patterns consisting of

Set of elements: <Area, Timestamp> and A recurrence formula: rec1.G1, …, recn.Gn,

Depict frequent user movement patterns <Home, 8am>, <Park, 8:30am>, <Work, 9am>, 1.day, 5.week

Personal History Locations (PHL): Sequence of element (x, y, t) that indicate the location (x, y) of a user U at

time t.

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…

Historical k-anonymity: A set of request R of user U is historical k-anonymity if

there exist k-1 PHLs P1, …, Pk-1for k-1 users other than U, such that each Pi is LS-consistent with R.

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Request: A tuple R = (x, y, t, S), S is service-specific data.

Element matching: User request Ri = (x, y, t, S) matches an element E of an LBQID if Ǝ

(x, y) ϵ E.coord and t ϵ E.timestamp R = (park, 8 :30am) … <Park, 8:30 am>, …

E Request LBQID matching:

A set of user requests R match his/her LBQID iff: Each request matches an element E and All requests satisfy the recurrence formula.

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LT-consistency: A PHL is Location and Time consistent with a set of request R if: Each request ri exists an element in the PHL or Request was sent at a time/location that can be extracted

from consecutive elements of PHL.

When a user U sends a set of request R, (historical) k-anonymity is preserved if at least k-1 user, other than U, have PHLs that are LT-consistent with R.

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Memorization PropertyHistorical k-Anonymity Algorithm

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Input: The ST information (x, y, t) of the request R. The desired level of anonymity (k). The spatial and temporal constraints.

Output: The generalized 3D area. A boolean value b to denote success/failure. A list N of the k-1 neighbors (after execution of the first-element

matching phrase)

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Problems to considers: LTS has to generate each request when it is issued

without knowledge of future locations and future request of users.

The longer PHL traces require, the more computational costs.

Our approach: PHLs of user are predefined (testing only), not updated

at real time. Only consider short PHL trace.

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Memorization PropertySummary & Work Flow

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Memorization is the 2nd property we consider. Memorization property checking is after

Reciprocity property checking. Memorization property checking covers 3 phases:

Check Maximum Movement Boundary Attack. Check Query Tracking Attack. Check Frequent Pattern Attack.

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Memorization PropertySummary & Work Flow

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Memorization is the 2nd property we consider.

Memorization property checking is after Reciprocity property checking.

Memorization property checking initially covers 3 phases: P1: Check Maximum Movement Boundary Attack. P2: Check Query Tracking Attack. P3: Check Frequent Pattern Attack. If the request is failed in any phase, the algorithm stops and

report the result to the next property checking.

Memorization Property

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