Abstract
This article represents a dynamic grid system (DGS), a privacy grid system defined by the user. This is the primary all-encompassing secure and spatial data satisfying basic essential necessities for confidentiality-securing snapshot and location-based services (LBSs). First, secure and spatial data are responsible for achieving simple matching operation using a semi-trusted third party. The semi-trusted third party has no information about the location of the user. Second, under the defined adversary model, we can provide a secured snapshot and uninterrupted location-based services. Third, not beyond the proximity of the user’s area, the communication cost does not rely on others ideal confidentiality location; it depends on the number of pertinent salient activities. Fourth, despite these things, it has only been targeted on the range and our system that can effectively support different spatial queries without altering the algorithms that are kept running by the semi-reliable third parties and the database servers, given that the spatial query is abstracted into spatial regions within the desired search area. The experimental assessment shows a more efficient approach towards the dynamic grid structure than the progressive confidentiality technique for uninterrupted location-based services. We offer a dual spatial data transformation and encryption scheme in which encrypted requests are executed completely on the encrypted database by the provider, and the user gets encrypted results. To attain services found on their location, location-based services want users to consistently report their locations to a potentially unreliable server which may open them to security risks. Lamentably, there were many restrictions in the existing confidentiality-securing methods for location-based services, such as the trustworthy third party requirement, confidentiality restrictions and high overhead communication.
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28 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-02168-3
References
Bamba B, Liu L, Pesti P, Wang T. Supporting anonymous location queries in mobile environments with PrivacyGrid. In: WWW. 2008.
Chow C.-Y, Mokbel MF. Enabling private continuous queries for revealed user locations. In: SSTD. 2007.
Gedik B, Liu L. Protecting location privacy with personalized kanonymity: architecture and algorithms. IEEE TMC. 2008;7(1):1–18.
Gruteser M, Grunwald D. Anonymous Usage of location-based services through spatial and temporal cloaking. In: ACM MobiSys. 2003.
Kalnis P, Ghinita G, Mouratidis K, Papadias D. Preventing location-based identity inference in anonymous spatial queries. IEEE TKDE. 2007;19(12):1719–33.
Mokbel MF, Chow C.-Y, Aref WG. The new casper: query processing for location services without compromising privacy. In: VLDB. 2006.
Xu T, Cai Y. Location anonymity in continuous location-based services. In: ACM GIS. 2007.
Exploring historical location data for anonymity preservation in location-based services. In: IEEE INFOCOM. 2008.
Ghinita G, Kalnis P, Khoshgozaran A, Shahabi C, Tan K.-L. Private queries in location based services: anonymizers are not necessary. In: ACM SIGMOD. 2008.
Kohlweiss M, Faust S, Fritsch L, Gedrojc B, Preneel B. Efficient oblivious augmented maps: location-based services with a payment broker. In: PET. 2007.
Vishwanathan R, Huang Y. A two-level protocol to answer private location-based queries. In: ISI. 2009.
Agrawal R, Kiernan J, Srikant R, Xu Y. Order-preserving encryption for numeric data. In: ACM SIGMOD. 2004.
Mykletun E, Tsudik G. Aggregation queries in the database-as-aservice model. In DBSec. 2006.
Xu T, Cai Y. Feeling-based location privacy protection for locationbased services. In: ACM CCS. 2009.
Khoshgozaran A, Shahabi C. Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In: SSTD.2007.
Hacigu¨mu¨s H¸ Iyer B, Mehrotra S. Efficient execution of aggregation queries over encrypted relational databases. In: DASFAA. 2004.
Palanisamy B, Liu L. Mobimix: protecting location privacy with mix zones over road networks. In: IEEE ICDE. 2011.
Mascetti S, Bettini C, Wang XS, Freni D, Jajodia S. ProvidentHider: an algorithm to preserve historical k-anonymity in LBS. In: MDM. 2009.
Dewri R, Ray I, Ray I, Whitley D. Query m-Invariance: preventing query disclosures in continuous location-based services. In: MDM. 2010.
Hoh B, Iwuchukwu T, Jacobson Q, Work D, Bayen AM, Herring R, Herrera JC, Gruteser M, Annavaram M, Ban J. Enhancing privacy and accuracy in probe vehicle-based traffic monitoring via virtual trip lines. IEEE TMC. 2012;11(5):849–64.
Pingley A, Zhang N, Fu X, Choi H.-A, Subramaniam S, Zhao W. Protection of query privacy for continuous location based services. In: IEEE INFOCOM. 2011.
Schlegel Roman, Chow Chi-Yin, Huang Qiong, Wong Duncan S. User-defined privacy grid system for continuous location-based services. IEEE Trans Mob Comput. 2015;14(10):2158–72.
Boldyreva A, Chenette N, ONeill A. Order-preserving encryption revisited: improved security analysis and alternative solutions. In: Rogaway P, editor. Advances in Cryptology–CRYPTO 2011. Berlin: Springer; 2011. p. 578–95.
Pub NF. 197: advanced encryption standard. Fed Inf Process Stand Publ. 2001;197(441):0311.
Ali Mazhar, Dhamotharan Revathi, Khan Eraj, Khan Samee U, Vasilakos Athanasios V, Zomaya Albert Y, Li Keqin. SeDaSC: secure data sharing in clouds. IEEE Syst J. 2017;11(2):395–404.
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Reddy, N.C.S., Madhuravani, B. & Sneha, D.P. An Approach for Efficient and Secure Data Encryption Scheme for Spatial Data. SN COMPUT. SCI. 1, 117 (2020). https://doi.org/10.1007/s42979-020-0095-8
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DOI: https://doi.org/10.1007/s42979-020-0095-8