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Data sharing and privacy protection of mobile users have always been a challenge

to research and development, as well as commercial and enterprise deployment of

the Global Positioning System (GPS) and location-based mobile applications. The

concepts of k-anonymity, two spatial cloaking algorithms—Nearest Neighbor

Cloak (NNC) and Hilbert Cloak (HC)—that utilize k-anonymity, as well as user

density’s impacts on the performance are discussed in this research . The

proposed research seeks to examine and adopt an adaptive scheme that utilizes

these cloaking algorithms to improve security, privacy protection and

performance of a system, using k-anonymity to generate the k-ASR in an

anonymizer.

ABSTRACT

Nowadays smart phones and other mobile devices are becoming increasingly ubiquitous.

These mobile devices with positioning capabilities utilize technologies such as the Global

Positioning System (GPS) that enables and facilitates Location Based Services (LBS). In these

services the need to protect user location privacy and user confidentiality is a necessity. One

of the techniques used to satisfy such necessity is spatial cloaking which is the process of

anonymizing a user’s location to a degree in which the likelihood of inferring it is very low.

This leads to the topic of k-anonymity, a highly-adopted concept used in spatial cloaking.

Location obfuscation can be used to obscure and conceal the actual query location of a user by

expanding his coordinates to a larger space.

We plan to implement the k-anonymity mechanism in an R*-tree indexed database and to

conduct experiments with an attempt to confirm whether the proposed system can achieve

improved performance. The proposed research seeks to adopt an adaptive scheme that utilizes

cloaking algorithms such as Nearest Neighbor and Hilbert to improve the security, privacy

protection and performance of a system.

INTRODUCTION

EXPERIMENTALRESULTS

Assuming NNC and HC behave significantly differently depending on the user density, it

would then be advantageous to select a different cloaking method for users with different

density. We conducted experiments to confirm the idea. The dataset used for the experiments

was based on California in the U.S. The number of users used was one third of the number of

points of interest (POI), randomly chosen to be within ten meters from the location of a POI.

We first computed the user density for each user. Then we isolated the one thousand users with

the highest density and another thousand users with the lowest density. The user density was

based on the total number of other users that were within a three kilometer circle from a user.

Using NNC and HC, we computed the size of the k-ASR and also the number of POI that were

located inside the k-ASR for both the high and low density users. The k used was eighty as it

provided a sufficient amount of anonymity for most users. The results (see Fig. 4) show that

the NNC has a smaller ASR area than that of HC for both high and low user density. The

average ASR of NNC for high density users is 19.25 sq km and 1838.17 sq km for the low

density users. On the other hand, the average ASR of HC for the high density users is 108.89

sq km and 3322.65 sq km for the low density users. Since NNC performs better than HC in

both high and low user density, our current experimental outcomes illustrates that user density

does not provide a beneficial indicator for selection of a cloaking method.

REFERENCES

Hilbert Cloak (HC): Hilbert cloak, another spatial cloaking algorithm, utilizes the concept of

k-anonymity to generate k-ASR in an anonymizer. First, with a query from a user u and also the

anonymity value k, HC computes and sorts the Hilbert curve values of all the users. It then

separates the users into buckets in which each bucket has exactly k users (except the last bucket

which may have up to 2k-1 users so that no bucket will have less than k users). That is, HC

partitions the user population into many k-ASRs by obtaining the Hilbert value H(u) of every

user. This is achieved by employing the Hilbert space-filling curve. The curve converts the two

dimensional coordinates of all users into single dimensional Hilbert values. The conversion

guarantees with a high probability that any two locations will remain in nearby vicinity in the

one dimensional space if they were in close vicinity in the two dimensional region. Finally,

given H(u) and rank(u), HC identifies the k-bucket containing rank(u) and then the MBR,

which encompasses the k users in the bucket, becomes the k-ASR. Using rank(u), the initial

position as well as the end position establishing the k-bucket can be computed as the following:

initial = rank(u) − (rank(u) mod k), end = initial + k − 1

Nearest Neighbor Cloak (NNC): Utilizing k-anonymity, NNC is one of the algorithms used

by an anonymizer for spatial cloaking. When a user U initiates a query, NNC computes the set

S0 which will include user U’s k-1 closest users (Fig. 2). Next, a random user Ui is chosen from

the set S0 and then a new set S1 is computed to contain Ui and his closest k-1 users. The final set

S2 will be the union set of user U and the set S1 . Hence, the k-ASR becomes the MBR

(minimum bounding rectangle) that covers all the users of the anonymized set S2. Due to the

randomness of the algorithm, the probability that user U is at or close to the center of the k-

ASR is remote. Thus, NNC is not vulnerable to the center-of-ASR attack which other cloaking

algorithms such as Center Cloak are exposed to.

Fig. 1. A framework to achieve k-anonymity.

Fig. 4. Preliminary experiment results.

MaBhewChan,ComputerInfo.Sys.Dept,BoroofManhaBanComm.College,CUNYHassanElsherbini,DepartmentofComputerScience,CollegeofStatenIsland,CUNYXiaowenZhang,DepartmentofComputerScience,CollegeofStatenIsland,CUNY

UserDensityandSpaNalCloakingAlgorithmSelecNon:ImprovingPrivacyProtecNonofMobileUsers

S. Papadopoulos, S. Bakiras, and D. Papadias, "Nearest neighbor search with strong location privacy," in Proceedings of the VLDB (Very Large Data Bases) Endowment, 3(1-2), pp. 619-629, 2010.

A. Pingley, W. Yu, N. Zhang, X. Fu, and W. Zhao, "Cap: A context-aware privacy protection system for location-based services," in Distributed Computing Systems, ICDCS'09, 29th IEEE International Conference, pp. 49-57, 2009.

B. Niu, Q. Li, X. Zhu, G. Cao, and H. Li, "Achieving k-anonymity in privacy-aware location-based services," in IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pp. 754-762, 2014.

L. Sweeney, "k-anonymity: a model for protecting privacy," in International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, vol. 10, no. 5, pp. 557–570, 2002.

C. C. Aggarwal, “On k-Anonymity and the curse of dimensionality,” in Proceeding of Very Large Data Bases (VLDB), pp. 901–909, 2005.

M. F. Mokbel, W. G. Aref, and I. Kamel, “Analysis of multi-dimensional space-filling curves,” in GeoInformatica, vol. 7, no. 3, pp. 179–209, 2003.

SPATIALCLOAKINGALGORITHMS

K-ANONYMITY

k-anonymity is one of the most adopted methods used to tackle the issues of protecting

individual privacy while sharing the data, and at the same time, maintaining the usefulness

and accuracy of the data. In LBS k-anonymity is widely adopted to prevent identity

compromise via location querying. First, a user sends his query and location information to an

anonymizer, a secured server which serves as a middle man between the user and the LBS

provider. In some cases (e.g. P2P network), an anonymizer could be part of user device. Next,

the anonymizer removes the id (a unique identity number) of the user and constructs a k-ASR

or circle encompassing the location of the query issuer with k-1 other user locations, to cloak

the user’s location. Finally, the generated k-ASR is sent to the LBS provider, which processes

and returns a set of candidates. The anonymizer then removes the false hits from the set and

forwards the results to the user.

Fig. 2. NNC and S0 , S1 , and S2.

Fig. 3. Hilbert curves of first, second and third order.