QUICK DESIGN GUIDE QUICK TIPS (--THIS SECTION DOES NOT PRINT--) User Density and Spa al Cloaking Algorithm Selec on: (--THIS SECTION DOES NOT PRINT--)
This PowerPoint 2007 template produces a 36”x48” professional poster. Improving Privacy Protec on of Mobile Users This PowerPoint template requires basic PowerPoint It will save you valuable time placing titles, subtitles, text, and (version 2007 or newer) skills. Below is a list of graphics. Ma hew Chan, Computer Info. Sys. Dept, Boro of Manha an Comm. College, CUNY commonly asked questions specific to this template. Hassan Elsherbini, Department of Computer Science, College of Staten Island, CUNY If you are using an older version of PowerPoint some Use it to create your presentation. Then send it to Xiaowen Zhang, Department of Computer Science, College of Staten Island, CUNY template features may not work properly. PosterPresentations.com for premium quality, same day affordable printing. Using the template ABSTRACT We provide a series of online tutorials that will guide you through the poster design process and answer your poster production questions. Verifying the quality of your graphics Data sharing and privacy protection of mobile users have always been a challenge Go to the VIEW menu and click on ZOOM to set your View our online tutorials at: to research and development, as well as commercial and enterprise deployment of preferred magnification. This template is at 100% the size of the final poster. All text and graphics will http://bit.ly/Poster_creation_help the Global Positioning System (GPS) and location-based mobile applications. The (copy and paste the link into your web browser). be printed at 100% their size. To see what your concepts of k-anonymity, two spatial cloaking algorithms—Nearest Neighbor poster will look like when printed, set the zoom to For assistance and to order your printed poster call Cloak (NNC) and Hilbert Cloak (HC)—that utilize k-anonymity, as well as user 100% and evaluate the quality of all your graphics Fig. 3. Hilbert curves of first, second and third order. before you submit your poster for printing. PosterPresentations.com at 1.866.649.3004 Fig. 1. A framework to achieve k-anonymity. density’s impacts on the performance are discussed in this research . The
proposed research seeks to examine and adopt an adaptive scheme that utilizes EXPERIMENTAL RESULTS Using the placeholders SPATIAL CLOAKING ALGORITHMS To add text to this template click inside a these cloaking algorithms to improve security, privacy protection and Assuming NNC and HC behave significantly differently depending on the user density, it placeholder and type in or paste your text. To move Object Placeholders Nearest Neighbor Cloak (NNC): Utilizing k-anonymity, NNC is one of the algorithms used performance of a system, using k-anonymity to generate the k-ASR in an would then be advantageous to select a different cloaking method for users with different a placeholder, click on it once (to select it), place by an anonymizer for spatial cloaking. When a user U initiates a query, NNC computes the set your cursor on its frame and your cursor will change Use the placeholders provided below to add new elements to your anonymizer. density. We conducted experiments to confirm the idea. The dataset used for the experiments S0 which will include user U’s k-1 closest users (Fig. 2). Next, a random user Ui is chosen from to this symbol: Then, click once and drag it to was based on California in the U.S. The number of users used was one third of the number of poster: Drag a placeholder onto the poster area, size it, and click it to its new location where you can resize it as needed. the set S0 and then a new set S1 is computed to contain Ui and his closest k-1 users. The final set edit. INTRODUCTION points of interest (POI), randomly chosen to be within ten meters from the location of a POI. Additional placeholders can be found on the left S2 will be the union set of user U and the set S1 . Hence, the k-ASR becomes the MBR We first computed the user density for each user. Then we isolated the one thousand users with side of this template. Section Header placeholder Nowadays smart phones and other mobile devices are becoming increasingly ubiquitous. (minimum bounding rectangle) that covers all the users of the anonymized set S2. Due to the the highest density and another thousand users with the lowest density. The user density was Move this preformatted section header placeholder to the poster area These mobile devices with positioning capabilities utilize technologies such as the Global Modifying the layout randomness of the algorithm, the probability that user U is at or close to the center of the k- based on the total number of other users that were within a three kilometer circle from a user. to add another section header. Use section headers to separate topics Positioning System (GPS) that enables and facilitates Location Based Services (LBS). In these This template has four or concepts within your presentation. ASR is remote. Thus, NNC is not vulnerable to the center-of-ASR attack which other cloaking Using NNC and HC, we computed the size of the k-ASR and also the number of POI that were services the need to protect user location privacy and user confidentiality is a necessity. One different column layouts. algorithms such as Center Cloak are exposed to. located inside the k-ASR for both the high and low density users. The k used was eighty as it Right-click your mouse of the techniques used to satisfy such necessity is spatial cloaking which is the process of provided a sufficient amount of anonymity for most users. The results (see Fig. 4) show that on the background and anonymizing a user’s location to a degree in which the likelihood of inferring it is very low. the NNC has a smaller ASR area than that of HC for both high and low user density. The click on “Layout” to see Text placeholder This leads to the topic of k-anonymity, a highly-adopted concept used in spatial cloaking. the layout options. Move this preformatted text placeholder to the poster to add a new average ASR of NNC for high density users is 19.25 sq km and 1838.17 sq km for the low Location obfuscation can be used to obscure and conceal the actual query location of a user by The columns in the provided layouts are fixed and body of text. density users. On the other hand, the average ASR of HC for the high density users is 108.89 cannot be moved but advanced users can modify any expanding his coordinates to a larger space. sq km and 3322.65 sq km for the low density users. Since NNC performs better than HC in layout by going to VIEW and then SLIDE MASTER. We plan to implement the k-anonymity mechanism in an R*-tree indexed database and to both high and low user density, our current experimental outcomes illustrates that user density conduct experiments with an attempt to confirm whether the proposed system can achieve Importing text and graphics from external sources does not provide a beneficial indicator for selection of a cloaking method. improved performance. The proposed research seeks to adopt an adaptive scheme that utilizes TEXT: Paste or type your text into a pre-existing Picture placeholder placeholder or drag in a new placeholder from the cloaking algorithms such as Nearest Neighbor and Hilbert to improve the security, privacy Move this graphic placeholder onto your poster, size it first, and then left side of the template. Move it anywhere as Fig. 2. NNC and S0 , S1 , and S2. click it to add a picture to the poster. protection and performance of a system. needed. PHOTOS: Drag in a picture placeholder, size it first, Hilbert Cloak (HC): Hilbert cloak, another spatial cloaking algorithm, utilizes the concept of K-ANONYMITY click in it and insert a photo from the menu. k-anonymity to generate k-ASR in an anonymizer. First, with a query from a user u and also the TABLES: You can copy and paste a table from an k-anonymity is one of the most adopted methods used to tackle the issues of protecting anonymity value k, HC computes and sorts the Hilbert curve values of all the users. It then external document onto this poster template. To separates the users into buckets in which each bucket has exactly k users (except the last bucket adjust the way the text fits within the cells of a individual privacy while sharing the data, and at the same time, maintaining the usefulness Fig. 4. Preliminary experiment results. which may have up to 2k-1 users so that no bucket will have less than k users). That is, HC table that has been pasted, right-click on the table, and accuracy of the data. In LBS k-anonymity is widely adopted to prevent identity click FORMAT SHAPE then click on TEXT BOX and partitions the user population into many k-ASRs by obtaining the Hilbert value H(u) of every REFERENCES compromise via location querying. First, a user sends his query and location information to an change the INTERNAL MARGIN values to 0.25 user. This is achieved by employing the Hilbert space-filling curve. The curve converts the two S. Papadopoulos, S. Bakiras, and D. Papadias, "Nearest neighbor search with strong location privacy," anonymizer, a secured server which serves as a middle man between the user and the LBS in Proceedings of the VLDB (Very Large Data Bases) Endowment, 3(1-2), pp. 619-629, 2010. provider. In some cases (e.g. P2P network), an anonymizer could be part of user device. Next, dimensional coordinates of all users into single dimensional Hilbert values. The conversion A. Pingley, W. Yu, N. Zhang, X. Fu, and W. Zhao, "Cap: A context-aware privacy protection system for Modifying the color scheme location-based services," in Distributed Computing Systems, ICDCS'09, 29th IEEE International To change the color scheme of this template go to the anonymizer removes the id (a unique identity number) of the user and constructs a k-ASR guarantees with a high probability that any two locations will remain in nearby vicinity in the Conference, pp. 49-57, 2009. the “Design” menu and click on “Colors”. You can one dimensional space if they were in close vicinity in the two dimensional region. Finally, B. Niu, Q. Li, X. Zhu, G. Cao, and H. Li, "Achieving k-anonymity in privacy-aware location-based services," or circle encompassing the location of the query issuer with k-1 other user locations, to cloak in IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pp. 754-762, 2014. choose from the provide color combinations or you the user’s location. Finally, the generated k-ASR is sent to the LBS provider, which processes given H(u) and rank(u), HC identifies the k-bucket containing rank(u) and then the MBR, L. Sweeney, "k-anonymity: a model for protecting privacy," in International Journal of Uncertainty, Fuzziness can create your own. and Knowledge-based Systems, vol. 10, no. 5, pp. 557–570, 2002. and returns a set of candidates. The anonymizer then removes the false hits from the set and which encompasses the k users in the bucket, becomes the k-ASR. Using rank(u), the initial C. C. Aggarwal, “On k-Anonymity and the curse of dimensionality,” in Proceeding of Very Large Data Bases (VLDB), pp. 901–909, 2005. forwards the results to the user. position as well as the end position establishing the k-bucket can be computed as the following: © 2012 PosterPresenta ons.com M. F. Mokbel, W. G. Aref, and I. Kamel, “Analysis of multi-dimensional space-filling curves,” in 2117 Fourth Street , Unit C initial = rank(u) − (rank(u) mod k), end = initial + k − 1 GeoInformatica, vol. 7, no. 3, pp. 179–209, 2003. Student discounts are available on our Facebook page. Berkeley CA 94710 RESEARCH POSTER PRESENTATION DESIGN © 2012 Go to PosterPresentations.com and click on the FB icon. www.PosterPresentations.com [email protected]