OST-Tree: An Access Method for Obfuscating Spatio-Temporal Data in Location Based Services

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1 OST-Tree: An Access Method or Ouscating Spatio-Temporal Data in Location Based Services Quoc Cuong TO, Tran Khanh DANG * Faculty o Computer Science & Engineering, HCMUT Ho Chi Minh City, Vietnam {qcuong, Jose KÜNG FAW Institute, Johannes Kepler University Linz, Austria Astract Since the development o location-ased services, privacy-preserving has gained special attention and many algorithms aiming at protecting user s privacy have een created such as ouscation or k-anonymity. However, all o these researches separate the algorithms rom the dataase level. Thus, the querying process has two phases, querying the dataase to retrieve the accurate positions o users and then modiying them to decrease the quality o location inormation. This two-phase process is time-consuming due to the numer o disk accesses required to retrieve the user s exact position. We address this prolem y proposing OST-tree, a structure that emeds the user s privacy policy in its node and ouscates the spatiotemporal data. Experiments show that OST-tree provides an improvement over the algorithm separated rom the dataase level or oth querying costs and user s privacy protection. Keywords-index; spatio-temporal; ouscation; LBS; privacy I. INTRODUCTION With the rapid development o gloal positioning system (GPS), there are more than 4.5 illion moile users y the year 2009 and the numer is expected to increase more. Among the various services or moile phone, the location-ased service (LBS) is the most promising one since it supplies users with many value-added services. In order to eneit rom these services, users, however, have to reveal their sensitive inormation such as their current locations. Such novel services pose many challenges ecause users are not willing to reveal their sensitive inormation ut still want to eneit rom these useul services. To solve this privacy-preserving prolem, a variety o algorithms are suggested to hide personal inormation o users ut still allow them to use services with acceptale quality. The general idea o these algorithms is to ouscate the user s position [3,4,16], or to anonymize location inormation [1,2]. There are two limitations o these algorithms. First, all o them deal with only spatial ouscation, ut not temporal one. Second, these algorithms are separated rom the dataase level. This separation makes the algorithms go through two phases: retrieving the exact location o user on the dataase level irst, and then ouscating this inormation on the algorithm level. This two-phase process is time-consuming due to the numer o disk access required to retrieve user s exact position. Motivated y this, in this work, we propose a new spatiotemporal index structure ased on TPR-tree [5] that can contain privacy-preserving inormation on it. This index structure deals with not only spatial ut also temporal ouscation, so we term it OST-tree, where OST stands or Ouscating Spatio- Temporal data. Furthermore, ecause this index structure emeds privacy inormation on its node, the process o calculating the ouscated data can e done in only one phase: traversing the index structure to retrieve the appropriately ouscated data. This one-phase process can reduce the processing time consideraly comparing to the two-phase process mentioned aove. In location-ased services environment, each user uses many services rom various service providers. For each service provider, user just wants to reveal his location in a speciic degree o accuracy depending on the user s trust on service providers. For example, users are willing to reveal their exact location to emergency services, ut only reveal their ouscated locations to advertising services. Thereore, classiication o location-ased service providers according to user s trust is very necessary in order not to violate privacy policy o users. Towards this goal, OST-tree is designed to eature service provider classiication y letting users speciy authorizations and put the privacy inormation on the tree nodes. The crucial contriutions o this paper are to deine the new concept o temporal ouscation, and to emed user s privacy policy into TPR-tree. This new index is capale o ouscating spatio-temporal data and provides an improvement over the algorithm separated rom the dataase level or oth querying costs and user s privacy protection. The rest o the paper is organized as ollows. Section 2 reviews related work. Section 3 introduces the new concept o temporal ouscation, and the authorization model. Section 4 presents our OST-tree, privacy overlaying, query processing algorithms and related analyses. Section 5 shows experimental results. Finally, section 6 concludes the paper. II. RELATED WORK A. Location Ouscation Among the most popular techniques to protect user s location, ouscation gains much interest [10,11,16]. Location ouscation aims at hiding user s exact location y decreasing the quality o user s location inormation. In [3], ouscation techniques are proposed y enlarging the area containing user s exact position. The igger the area, the harder an attacker can iner the user s exact location. I the area, however, is too ig, (*) Part o this research had een done as the author was at FAW institute, Johannes Kepler University (JKU), Linz, Austria.

2 it can aect the quality o location-ased services. So, it is the responsiility o user to decide which degree o accuracy o user s location to e revealed to which service providers. Motivated y this, Dang et al. developed the general architecture [7] to classiy LBS service providers depending on the user s trust. This architecture inherits the property o mandatory access control to lael service providers so that users only reveal their locations on an appropriate level ased on the laels assigned to service providers. However, the index structure in this architecture does concern aout temporal data at a very astract level. Thus, it is necessary to concretize this structure y a suitale spatio-temporal index and this will e discussed in the next section. B. Spatio-Temporal Structures or Indexing the Present and Future Positions o Moving Ojects Several recent researches ocus on indexing the present and uture positions o moving ojects [12] and the most popular category is parametric spatial access. Two popular access methods in this category are PR-tree and TPR-tree. PR-tree [14], however, is only suitale or ojects with spatial extent. So, in applications concerning a user s position which is a spatial point in nature, the PR-tree is not the est solution. For TPR-tree [5], it inherits the idea o parametric ounding rectangles in R-tree [15] to create time-parameterized ounding rectangle (tpr). Since the tpr is organized in hierarchical orm in terms o space, TPR-tree is chosen as the ase structure o our proposed structure so that we can easily overlay the ouscated data in TPR-tree s node hierarchically. In TPR-tree, the position o an moving oject x(t) at a uture time t (t >= t c ) is ound y applying the linear unction representing its location to the current time x(t) = x(t 0 ) + v(t t 0 ) where t 0 is the initial time, t c the current time, x(t 0 ) the initial position and v the velocity. The tpr is also a unction o time. Speciically, the lower (upper) ound o a tpr is set to move with the minimum (maximum) speed o all enclosed ojects. Despite the existence o several indexing techniques or present and uture positions, no moving-oject index has yet een reported in the literature that achieves the goal o ouscating the user s position. C. Access Methods or Privacy-Preserving Several index structures have een proposed to manage oth proiles and moving oject data. The S STP -tree [6] is constructed similarly to the TPR-tree, ut each node has additional inormation aout a proile ounding vector to support the proile conditions. Thereore, each node o the S STP -tree includes oth tpr to support the spatio-temporal attriutes and proile ounding vector to support proile conditions. The limitation o this access method is that it only allows or denies the access request o sujects, ut does not concern aout ouscating the spatio-temporal data. In other words, there are only two levels o result in the access request evaluation: reject or accept. By adding more inormation aout ouscating the spatio-temporal data o users, our proposed index structure, however, has a multi-level orm o result when evaluating an access request depending on the user s trust on the LBS service providers. In [13], a uniied index or location and proile data is proposed. This index clusters the customers ased on their proiles using a categorical clustering algorithm, and then constructs a TPR-tree or each cluster. A query is processed in the proile dataase to retrieve the target clusters and then traverse these clusters to retrieve the customers who satisy the criteria. This uniied index is, however, used or marketing purpose which retrieves the group o interested customers, ut does not concern aout ouscating the customer s location. It is evident rom the aove discussion that currently there does not exist any spatio-temporal index structure that can eectively handle spatio-temporal ouscation. Towards this goal, in this paper, we propose the OST-tree, a structure originally motivated y the TPR-tree, ut with several modiications to support spatio-temporal ouscation. III. TEMPORAL OBFUSCATION Many o the research activities have een done in the area o spatial ouscation [3,4,10,11,16], ut, to the est o our knowledge, no mature proposals or ouscating the temporal data o users exist. So, we ocus on this issue in this section. Similar to spatial ouscation, temporal ouscation will degrade the exact value o time t 0 to the vague temporal value [t [, t ] ], where t [ < t 0 < t ]. For example, instead o saying that the position o user will e in location (x 0, y 0 ) in the next 15 minutes, we can ouscate the time value y saying that the position o user will e in location (x 0, y 0 ) in the next 13 to 16 minutes. By comining the spatial and temporal dimension, a spatio-temporal value can e calculated y ouscating oth the spatial and temporal value. For example, according to the aove example, we can say: The user s position is somewhere in the area o 1.2 square kilometer, including the location (x 0, y 0 ), and within the next 13 to 16 minutes in the uture. Deinition 1. (Temporal ouscation) The ouscated value o timestamp t 0 is the temporal interval [t [, t ] ] which includes the real timestamp t 0 with the proaility: P(t 0 [t [, t ] ])=1 (1) Deinition 2. (Spatio-temporal ouscation) The ouscated value o user s exact position (xu, yu) at a timestamp t 0 is a rectangular area (xc, yc, w, h) centered on the geographical coordinates (xc, yc) with width w, height h, at a temporal interval [t [, t ] ], which includes the user s exact position (xu, yu) at a real timestamp t 0 with the proaility: P((x u, y u ) Rectangle(x c, y c, w, h) AND t 0 [t [, t ] ])=1 (2) In our work, we have the same assumption as in [10] which states that the proaility distriution o user s position within an area is uniorm. Formally, the joint proaility density unction r (x, y) o a region is: 1 i (x, y) r r(x, y)= s(r) 0 otherwise where s(r) represents the area o r. (3)

3 Similarly, the proaility distriution o user s position within an area r and at a time t 0 within an interval [t [, t ] ] is: (x,y)= r, t 1 1 ] [ s(r) (t t ) [ ] i (x, y) r, t [t,t ] 0 otherwise Deinition 3. (Authorization) An authorization α is a 4- tuple <id sp, id user, s, t> where id sp is the identity o service provider, id user is the identity o user, s, t is the degree o accuracy o user s position (spatial data) and time, respectively. The meaning o an authorization is that a user with the identity id user allows only the service provider with the identity id sp to access his/her sensitive inormation o position and time with the degree o accuracy o s, t, respectively. For example, a user with the identity #U232 is willing to reveal his position in the next 10 minutes with the accuracy o position and time eing 600 square meters, 3 minutes, respectively, to the advertising service with the identity #S101. This authorization can e expressed as α 1 = <#S101, #U232, 600m 2, 3m>. I the user s exact position in the next 10 minutes is located at a coordinate <x 0, y 0 >, the result returned rom the next 9 to 12 minutes to the service provider is a rectangle which has the area o 600 square meters and contains the coordinate <x 0, y 0 > in case o time and position, respectively. IV. INDEX STRUCTURE The ase structure o the OST-tree is that o the TPR-tree or indexing the spatio-temporal data. However, in order to speciy the authorization and the degree o accuracy o user s position and time, the node structure will e modiied to attach more inormation. Speciically, in addition to the tpr, each node contains a pointer p pointing to the list o entries. Each entry has the orm o a 4-tuple <id sp, id user, s, t>, indicating that a service provider with the identity id sp can access sensitive inormation o a user with the identity id user at the degree o accuracy o user s position and time speciied y the value s and t, respectively. Fig. 1 illustrates the structure o the OSTtree. For the illustration purpose, the values o authorizations α i (i=1..5) in this igure are α 1 = <#S101, #U232, 1000m 2, 3m>, α 2 = <#S101, #U134, 600m 2, 3m>, α 3 = <#S102, #U232, 500m 2, 3m>, α 4 = <#S101, #U135, 550m 2, 4m>, and α 5 = <#S103, #U232, 0m 2, 0m>. Our goal is to develop an index structure that can incorporate the accuracy degree o user s position. Thereore, this accuracy degree parameter must e in the hierarchical orm. The OST-tree achieves this hierarchy well. Since the tpr in a TPR-tree is already organized in hierarchical structure, the OST-tree inherits this property to hierarchically organize the ounding rectangle containing the user s exact position that will e returned to the service providers. More speciically, when traversing rom the root node to a lea node in the OSTtree, the degree o accuracy o user s position increases ecause the area o the ounding rectangle is smaller and vice versa. For example, in the traversal path N1-N5-N14 (see Fig. 1), the areas o the returned rectangles reduce rom 1000m 2 to 500m 2 and 0m 2 corresponding to α 1, α 3, and α 5. This means that the degree o accuracy o user s position increases. 0 (4) Based on this property, i service providers have a higher level o trust rom a user, their identities will e placed on the node nearer to the lea node and vice versa. For instance, the service provider with the identity #S103 has the highest level o trust rom a user with the identity #U232, and so it can otain the user s exact position ( s=0). This service provider s identity is, thereore, placed on the lea node. Figure 1. OST-tree structure A. Privacy Inormation Overlaying and Insertion The privacy inormation overlaying and insertion process happen in parallel. We traverse the OST-tree rom the root node down to the lea node to place the new oject in the suitale lea node (y applying the insertion algorithm as shown in [5,8]) and, at the same time, recursively compare the degree o accuracy o user s position ( s) with a spatial extent o each node (N ) in the insertion path to ind the appropriate node overlaying privacy inormation. We have two possile scenarios or this comparison: Case 1: I (N is the appropriate su-tree) and ( s N ), we overlay α on N and continue the insertion process. Case 2: I ( s< N ), depend on the level o N, we have two scenarios: I N is a non-lea node, we choose an appropriate su-tree rooted at N (complying with the algorithm ChooseSutree o R*-trees [8]) and continue the overlaying process. I N is a lea node, we overlay and insert the new oject into this node. I a moving oject has already existed in the index structure and the user wants to add new policies, we ind the appropriate node in the insertion path to overlay privacy inormation. Since the authorization is put as high as possile in the OST-tree, the search process can stop at some internal node i the match occurs. Thus, we do not always have to traverse to the leaves to ind a user s exact position as in algorithms separated rom the dataase level. For example, i the service provider #S101 wants to otain the position o user #U135, the search process stops at the internal node N6-N7 and returns the result. But, in the case o an algorithm separated rom the dataase level, we have to traverse to the lea node N15, where

4 the position o #U135 elongs to, to retrieve the user s exact position and then ouscate it. B. Privacy Analysis Adversary model: the adversary tries to manipulate the ouscated region to iner the user s exact location. For ouscation techniques, the relevance [11] is used to measure the location privacy protection. The lower the relevance, the higher the location privacy protection is, and thus the lower the proaility an adversary can iner the user s exact location. So, in order to analyze the location privacy protection o our proposed approach with that o the approach that separates the algorithm rom the dataase level, we will compare the relevance values o the two approaches. For the approach that separates the algorithm rom dataase level, the relevance is: 2 ( Ai A ) Rs = AA. i where A i is the location measurement [11] and depends completely on the positioning technology, and A is the ouscated region created y the privacy-preserving algorithm To calculate the relevance o our proposed approach, we can simply replace A y s in (5). However, the concept o relevance in [11] only concerns aout spatial privacy protection. By taking into account the temporal element, we extend the relevance concept to use or oth spatial and temporal privacy protection as ollows: 2 ( Ai Δs) 1 Rst =. (6) Ai. Δs Δt where s and t are the degree o accuracy o user s position and time, respectively. From (5) and (6), we can see that R s R st (since A = s and t 1), meaning that the degree o privacy protection o our proposed approach is higher than that o approach separating the algorithm rom dataase level. More speciic, incorporating the temporal dimension into the relevance concept reduces the proaility that an adversary can iner the user s exact location ecause the adversary has to guess not only where, ut also when the user s exact position elongs to. C. Perormance Analysis In this section, we compare the perormance etween the TPR-tree and OST-tree in terms o the numer o disk accesses. For the analysis, let us suppose that n is the numer o moving users; t is the size o each tpr; d is the disk lock size; M is the maximum numer o tree pointers in one node; P is the size o lock pointer pointing to a sutree; P r is the size o authorization pointer pointing to the list o authorizations; a is the average numer o authorization placed in each node; S a is the size o each authorization α. For the TPR-tree, an internal node contains only timeparameterized ounding rectangles and lock pointers; these must it into a single lock: (5) M ( P d ) d M P So, the numer o disk accesses is: d For the OST-tree, we have two cases: the list o authorizations is pointed y a pointer or emedded directly into the nodes. For the irst case, an internal node o OST-tree contains the time-parameterized ounding rectangles, lock pointers and an authorization pointer; these must it into a single lock: d Pa M( ) + Pa d M P So, the numer o disk accesses is: d Pa For the second case, an internal node will have the same numer o time-parameterized ounding rectangles and lock pointers, ut the size o authorization depends on the numer o authorization placed in each node. Hence, the order M can e calculated as ollows: d asa M ( ) + asa d M So, the numer o disk accesses is: d asa In OST-tree, i the authorization is emedded directly into the node, it will require less disk accesses than that o the irst case where the authorization is pointed y a pointer. Also, the aove analysis shows that when traversing to lea nodes is required in two indexes, the TPR-tree has the lower height and requires less disk accesses than that o the OST-tree since the OST-tree has to reserve the space to store the authorization in each node. However, in most cases, we do not have to traverse to the OST-tree leaves to retrieve the result. Because i the pair value <id sp, id user > o the query s authorization is matched with that o some internal node, we will stop at this node and return the result without urther traversing on the OST-tree. Hence, the OST-tree requires less disk accesses than that o the TPR-tree. Only in the worst case where users are willing to reveal their exact position to service providers, we have to traverse to the lea node to retrieve the exact result. For example, i the service provider #S101 wants to get position o the user #U134, the query needs visiting only two nodes (root node and N1) instead o three nodes, therey reducing the numer o disk accesses comparing to TPR-trees. V. PERFORMANCE EXPERIMENTS To conduct the experiments, we use the open source implementation o TPR-trees called SaIL [9]. Both TPR-tree (7) (8) (9)

5 and OST-tree are implemented in C++, and all the experiments are conducted on a Core 2 Duo Personal Computer with 1 GB o memory. In the experiments, we use uniorm data, where oject positions are randomly generated and speeds ranging rom 0.25 to 1.66 are chosen at random. The eective ill actor is usually close to 70%. The an-out o internal and lea nodes is 20 with a 4K page size. The maximum update interval is 20. The numer o query is 35 and the horizon time is 20. Since the node o OST-tree contains authorizations, the numer o records in each OST-tree s node is smaller than that o the TPR-tree. So, the OST-tree requires more nodes to contain the same numer o moving ojects (c. Fig. 2). Figure 2. Storage cost Figure 3. Relevance By incorporating the time into the privacy model, the average relevance o our proposed approach (R st ) is smaller than that o the ouscation algorithm (R s ) (c. Fig. 3). Fig. 4 and Fig. 5 compare the insert cost etween the TPRtree and OST-tree in terms o CPU time and numer o I/O operations, respectively. The insert cost o OST-tree is higher than that o TPR-tree since we have to spend extra time (or numer o I/O operations), esides the time or insertion process, to ind appropriate node to overlay authorization. Given the moility o users, the update cost as shown in Fig. 6 o OST-tree is higher than that o TPR-tree, ecause OST-tree has to incur the additional cost o updating the authorization (moving α rom current node to another node corresponding to the newly updated position o a user). Figure 4. Insert cost (CPU time) Figure 6. Update cost Figure 5. Insert cost (I/Os) Figure 7. Point location query cost Fig. 7 compares the query cost etween the two indexes in terms o the numer o I/O operations. The query in this case is point location queries, which retrieves the rectangle containing the location o user. In general, the query cost o OST-tree is etter than that o TPR-tree since we do not have to traverse to the lea node to get the result in OST-tree. Only in some cases, where users want to reveal their exact locations to service providers, the OST-tree is not etter than TPR-tree, ecause we have to traverse to the lea nodes o OST-tree. Hence, OST-tree is etter than TPR-tree in cases users just want to reveal a low degree o accuracy o their locations to service providers. VI. CONCLUSION AND FUTURE WORK In this work, we have introduced the OST-tree capale o ouscating the spatio-temporal data o users. Although the OST-tree requires more storage space and update overhead, it achieves the lower querying cost and higher privacy protection comparing to the TPR-tree. Future work will extend the proaility distriution o user s position so that the proaility that a user s position (x, y) elongs to a region is not uniormly distriuted. Because, in real lie, the region where a user elongs to depends on many actors related to geography, it is easy or the adversary to iner a user s exact position in the ouscated area i the proaility distriution o user s position is uniormly distriuted. REFERENCES [1] M. Gruteser, D. Grunwald: Anonymous Usage o Location-Based Services Through Spatial and Temporal Cloaking. MOBISYS, [2] G. Bugra, L. Ling: Protecting Location Privacy with Personalized k- Anonymity: Architecture and Algorithms. IEEETMC, 7(1):1 18, [3] C.A. Ardagna, M. Cremonini, E. Damiani, S.D.C. Vimercati, P. Samarati: Location-Privacy Protection through Ouscation-ased Techniques. DBSEC, [4] F.M. Mohamed: Privacy in Location-ased Services: State-o-the-art and Research Directions. MDM, [5] S. Saltenis, C.S. Jensen, S.T. Leutenegger, M.A. Lopez: Indexing the Positions o Continuously Moving Ojects. ACM SIGMOD, pp , [6] V. Atluri, H. Shin: Eicient Security Policy Enorcement in a Location Based Service Environment. DBSEC, [7] T.K. Dang, Q.C. To: An Extensile and Pragmatic Hyrid Indexing Scheme or MAC-ased LBS Privacy-Preserving in Commercial DBMSs. ACOMP, pp , [8] N. Beckmann, H.-P. Kriegel, R. Schneider, B. Seeger: The R * -tree: An Eicient and Roust Access Method or Points and Rectangles. ACM SIGMOD, pp , [9] M. Hadjieletheriou, E. Hoel, V.J. Tsotras: SaIL: A Spatial Index Lirary or Eicient Application Integration. Geoinormatica, 9(4): , [10] J. H. Jaarian, M. Amini, R. Jalili: Protecting Location Privacy through a Graph-ased Location Representation and a Roust Ouscation Technique. ICISC, [11] C.A. Ardagna, M. Cremonini, S.D.C. Vimercati, P. Samarati: An Ouscation-Based Approach or Protecting Location Privacy. TDSC, 8(1):13-27, [12] M.F. Mokel, T.M. Ghanem, W.G. Are: Spatio-temporal access methods. IEEE Data Engineering Bulletin, 26(2):40 49, [13] V. Atluri, N.R. Adam, M. Yousse: Towards a uniied index scheme or moile data and customer proiles in a location-ased service environment. NG2I, [14] M. Cai, P.Z. Revesz: Parametric R-Tree: An Index Structure or Moving Ojects. COMAD, [15] A. Guttman: R-trees: A Dynamic Index Structure or Spatial Searching. SIGMOD, pp , [16] T.T. Anh, T. Q. Chi, T.K. Dang: An Adaptive Grid-Based Approach to Location Privacy Preservation. ACIIDS, pp , 2010.

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