QUICK DESIGN GUIDE User Density and SpaNal Cloaking ... · poster: Drag a placeholder onto the...
Transcript of QUICK DESIGN GUIDE User Density and SpaNal Cloaking ... · poster: Drag a placeholder onto the...
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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.