{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:40:22Z","timestamp":1760136022447,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["813278"],"award-info":[{"award-number":["813278"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["328226"],"award-info":[{"award-number":["328226"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Future social networks will rely heavily on sensing data collected from users\u2019 mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user\u2019s location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.<\/jats:p>","DOI":"10.3390\/s22020687","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T20:49:21Z","timestamp":1642452561000},"page":"687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-6924","authenticated-orcid":false,"given":"Elena Simona","family":"Lohan","sequence":"first","affiliation":[{"name":"Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8178-5652","authenticated-orcid":false,"given":"Viktoriia","family":"Shubina","sequence":"additional","affiliation":[{"name":"Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland"},{"name":"Computer Science and Engineering Department, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7590-2015","authenticated-orcid":false,"given":"Drago\u0219","family":"Niculescu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26902","DOI":"10.1109\/ACCESS.2021.3053486","article-title":"Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges","volume":"9","author":"Belot","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"(2021, November 15). P802.11bf\u2014Standard for Information Technology\u2014Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks\u2014Specific Requirements\u2014Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment: Enhancements for Wireless Local Area Network (WLAN) Sensing. Available online: https:\/\/standards.ieee.org\/project\/802_11bf.html?utm_source=beyondstandards&utm_medium=post&utm_campaign=working-group-2020&utm_content=802."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Brovko, T., Chugunov, A., and Malyshev, A. (2021, January 5\u201311). Positioning Algorithm for Smartphone Based Staff Tracking. Proceedings of the 2021 International Russian Automation Conference (RusAutoCon), Sochi, Russia.","DOI":"10.1109\/RusAutoCon52004.2021.9537405"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Flueratoru, L., Shubina, V., Niculescu, D., and Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection. IEEE Sens. J.","DOI":"10.1109\/JSEN.2021.3095710"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1017\/S0373463321000175","article-title":"Effectiveness modelling of digital contact-tracing solutions for tackling the COVID-19 pandemic","volume":"74","author":"Shubina","year":"2021","journal-title":"J. Navig."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shubina, V., Holcer, S., Gould, M., and Lohan, E.S. (2020). Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era. Data, 5.","DOI":"10.3390\/data5040087"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bian, S., Zhou, B., and Lukowicz, P. (2020). Social Distance Monitor with a Wearable Magnetic Field Proximity Sensor. Sensors, 20.","DOI":"10.3390\/s20185101"},{"key":"ref_8","unstructured":"Vaudenay, S. (2021, November 24). Centralized or Decentralized? The Contact Tracing Dilemma. Available online: https:\/\/eprint.iacr.org\/2020\/531.pdf."},{"key":"ref_9","unstructured":"Castelluccia, C., Bielova, N., Boutet, A., Cunche, M., Lauradoux, C., Le M\u00e9tayer, D., and Roca, V. (2020, November 24). ROBERT: ROBust and privacy-presERving Proximity Tracing. Available online: https:\/\/hal.inria.fr\/hal-02611265\/document."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Leith, D.J., and Farrell, S. (2021, January 10\u201313). Contact tracing app privacy: What data is shared by europe\u2019s gaen contact tracing apps. Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOM42981.2021.9488728"},{"key":"ref_11","first-page":"1","article-title":"Location privacy-preserving mechanisms in location-based services: A comprehensive survey","volume":"54","author":"Jiang","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cosrev.2017.03.002","article-title":"Indoor location based services challenges, requirements and usability of current solutions","volume":"24","author":"Basiri","year":"2017","journal-title":"Comput. Sci. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"von Arb, M., Bader, M., Kuhn, M., and Wattenhofer, R. (2008, January 12\u201314). VENETA: Serverless Friend-of-Friend Detection in Mobile Social Networking. Proceedings of the 2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Avignon, France.","DOI":"10.1109\/WiMob.2008.52"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.future.2016.12.012","article-title":"The flexible and privacy-preserving proximity detection in mobile social network","volume":"79","author":"Ye","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102464","DOI":"10.1016\/j.cose.2021.102464","article-title":"A Survey of differential privacy-based techniques and their applicability to location-Based services","volume":"111","author":"Kim","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chatzikokolakis, K., Palamidessi, C., and Stronati, M. (2015, January 5\u20138). Geo-indistinguishability: A principled approach to location privacy. Proceedings of the International Conference on Distributed Computing and Internet Technology, Bhubaneswar, India.","DOI":"10.1007\/978-3-319-14977-6_4"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Andr\u00e9s, M.E., Bordenabe, N.E., Chatzikokolakis, K., and Palamidessi, C. (2013, January 4\u20138). Geo-indistinguishability: Differential privacy for location-based systems. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, Berlin, Germany, Berlin, Germany.","DOI":"10.1145\/2508859.2516735"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qiu, C., Squicciarini, A.C., Pang, C., Wang, N., and Wu, B. (2020). Location privacy protection in vehicle-based spatial crowdsourcing via geo-indistinguishability. IEEE Trans. Mobile Comput.","DOI":"10.1109\/TMC.2020.3037911"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104775","DOI":"10.1109\/ACCESS.2020.2999580","article-title":"Differential private spatial decomposition and location publishing based on unbalanced quadtree partition algorithm","volume":"8","author":"Yan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shubina, V., Ometov, A., Andreev, S., Niculescu, D., and Lohan, E.S. (2020, January 2\u20134). Privacy versus Location Accuracy in Opportunistic Wearable Networks. Proceedings of the2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland.","DOI":"10.1109\/ICL-GNSS49876.2020.9115424"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1515\/popets-2017-0051","article-title":"Efficient utility improvement for location privacy","volume":"2017","author":"Chatzikokolakis","year":"2017","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9173519","DOI":"10.1155\/2018\/9173519","article-title":"A context-aware location differential perturbation scheme for privacy-aware users in mobile environment","volume":"2018","author":"Zhang","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10707-013-0193-z","article-title":"User-side adaptive protection of location privacy in participatory sensing","volume":"18","author":"Agir","year":"2013","journal-title":"GeoInformatica"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"du Pin Calmon, F., and Fawaz, N. (2012, January 1\u20135). Privacy against statistical inference. Proceedings of the 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/Allerton.2012.6483382"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Salamatian, S., Zhang, A., Calmon, F.d.P., Bhamidipati, S., Fawaz, N., Kveton, B., Oliveira, P., and Taft, N. (2013, January 3\u20135). How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data. Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA.","DOI":"10.1109\/GlobalSIP.2013.6736867"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Degue, K.H., and Ny, J.L. (2018, January 2\u20135). On Differentially Private Gaussian Hypothesis Testing. Proceedings of the2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/ALLERTON.2018.8635911"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TIFS.2017.2779402","article-title":"A Geo-Indistinguishable Location Perturbation Mechanism for Location-Based Services Supporting Frequent Queries","volume":"13","author":"Hua","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, M., Zhu, X., Niu, J., and Ma, J. (2019, January 10\u201313). A Semantic-Based Dummy Generation Strategy for Location Privacy. Proceedings of the2019 International Conference on Networking and Network Applications (NaNA), Daegu, Korea.","DOI":"10.1109\/NaNA.2019.00013"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shekhar, S., and Xiong, H. (2008). Location Perturbation. Encyclopedia of GIS, Springer.","DOI":"10.1007\/978-0-387-35973-1_718"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gruteser, M., and Grunwald, D. (2003, January 5\u20138). Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. Proceedings of the 1st International Conference on Mobile Systems, Applications and Services\u2014MobiSys\u201903, San Francisco, CA, USA.","DOI":"10.1145\/1066116.1189037"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dini, G., and Perazzo, P. (2012). Uniform Obfuscation for Location Privacy. Data and Applications Security and Privacy XXVI, Springer.","DOI":"10.1007\/978-3-642-31540-4_7"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00779-008-0212-5","article-title":"A survey of computational location privacy","volume":"13","author":"Krumm","year":"2008","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, H., and Yu, X. (2016, January 23\u201326). Multiple Mix-Zones Deployment for Continuous Location Privacy Protection. Proceedings of the 2016 IEEE Trustcom\/BigDataSE\/ISPA, Tianjin, China.","DOI":"10.1109\/TrustCom.2016.0136"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7985","DOI":"10.1109\/JIOT.2020.3043640","article-title":"Cooperative Location Privacy in Vehicular Networks: Why Simple Mix Zones are Not Enough","volume":"8","author":"Khodaei","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., and Li, S. (2018, January 16\u201319). A Real-Time Location Privacy Protection Method Based on Space Transformation. Proceedings of the 2018 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, China.","DOI":"10.1109\/CIS2018.2018.00071"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pu, Y., Luo, J., Wang, Y., Hu, C., Huo, Y., and Zhang, J. (2018, January 26\u201328). Privacy Preserving Scheme for Location Based Services Using Cryptographic Approach. Proceedings of the2018 IEEE Symposium on Privacy-Aware Computing (PAC), Washington, DC, USA.","DOI":"10.1109\/PAC.2018.00022"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jarvinen, K., Leppakoski, H., Lohan, E.S., Richter, P., Schneider, T., Tkachenko, O., and Yang, Z. (2019, January 17\u201319). PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing. Proceedings of the2019 IEEE European Symposium on Security and Privacy (EuroS P), Stockholm, Sweden.","DOI":"10.1109\/EuroSP.2019.00040"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gupta, S., and Arora, G. (2019, January 21\u201322). Use of Homomorphic Encryption with GPS in Location Privacy. Proceedings of the 2019 4th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India.","DOI":"10.1109\/ISCON47742.2019.9036149"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2073","DOI":"10.1109\/TNSE.2020.3011607","article-title":"Perturbation-Hidden: Enhancement of Vehicular Privacy for Location-Based Services in Internet of Vehicles","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lu, H., Jensen, C.S., and Yiu, M.L. (2008, January 13). Pad: Privacy-area aware, dummy-based location privacy in mobile services. MobiDE\u201908 Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, Vancouver, BC, Canada.","DOI":"10.1145\/1626536.1626540"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bindschaedler, V., and Shokri, R. (2016, January 22\u201326). Synthesizing plausible privacy-preserving location traces. Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2016.39"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.procs.2021.02.002","article-title":"Efficient Combination of RSA Cryptography, Lossy, and Lossless Compression Steganography Techniques to Hide Data","volume":"182","author":"AbdelWahab","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3-es","DOI":"10.1145\/1217299.1217302","article-title":"l-diversity: Privacy beyond k-anonymity","volume":"1","author":"Machanavajjhala","year":"2007","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., and Venkatasubramanian, S. (2007, January 15\u201320). t-closeness: Privacy beyond k-anonymity and l-diversity. Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMC.2007.1062","article-title":"Protecting location privacy with personalized k-anonymity: Architecture and algorithms","volume":"7","author":"Gedik","year":"2007","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., and Yu, T. (2012, January 1\u20135). Differentially private spatial decompositions. Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, Arlington, VA, USA.","DOI":"10.1109\/ICDE.2012.16"},{"key":"ref_47","first-page":"9169802","article-title":"Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis","volume":"2019","author":"Xu","year":"2019","journal-title":"Secur. Commun. Networks"},{"key":"ref_48","unstructured":"Balle, B., and Wang, Y.X. (2018, January 10\u201315). Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. Proceedings of the 35th International Conference on Machine Learning, Stockholmsm\u00e4ssan, Stockholm Sweden."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/687\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:02:30Z","timestamp":1760133750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020687"],"URL":"https:\/\/doi.org\/10.3390\/s22020687","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,1,17]]}}}