{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:35:04Z","timestamp":1776083704041,"version":"3.50.1"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>With the proliferation of IoT cameras, it is possible to use crowdsourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle) on demand. Due to the ubiquity of IoT cameras such as dash mounted and phone cameras, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and, thus, can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homomorphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target\u2019s face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. We also formulate and solve a worker selection problem to maximize the probability of finding the target under some budget constraint. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.<\/jats:p>","DOI":"10.1145\/3375878","type":"journal-article","created":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T10:14:07Z","timestamp":1591006447000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["TargetFinder"],"prefix":"10.1145","volume":"1","author":[{"given":"Youssef","family":"Khazbak","sequence":"first","affiliation":[{"name":"The Pennsylvania State University, University Park, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junpeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"Facebook, Menlo Park, California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxiang","family":"Tan","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, University Park, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohong","family":"Cao","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, University Park, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2012. Java Lattice Based Cryptography Library. http:\/\/gas.dia.unisa.it\/projects\/jlbc\/index.html.  2012. Java Lattice Based Cryptography Library. http:\/\/gas.dia.unisa.it\/projects\/jlbc\/index.html."},{"key":"e_1_2_1_2_1","unstructured":"2013. Bouncy Castle. https:\/\/www.bouncycastle.org\/.  2013. Bouncy Castle. https:\/\/www.bouncycastle.org\/."},{"key":"e_1_2_1_3_1","unstructured":"2016. Caffe Android lib. Retrieved from https:\/\/github.com\/sh1r0\/caffe-android-lib.  2016. Caffe Android lib. Retrieved from https:\/\/github.com\/sh1r0\/caffe-android-lib."},{"key":"e_1_2_1_4_1","unstructured":"2017. Libgcrypt. Retrieved from https:\/\/www.gnupg.org\/software\/libgcrypt\/index.html.  2017. Libgcrypt. Retrieved from https:\/\/www.gnupg.org\/software\/libgcrypt\/index.html."},{"key":"e_1_2_1_5_1","unstructured":"2018. Dlib C++ library. Retrieved from http:\/\/dlib.net\/.  2018. Dlib C++ library. Retrieved from http:\/\/dlib.net\/."},{"key":"e_1_2_1_6_1","unstructured":"2018. Firebase Cloud Messaging. Retrieved from https:\/\/firebase.google.com\/docs\/cloud-messaging\/.  2018. Firebase Cloud Messaging. Retrieved from https:\/\/firebase.google.com\/docs\/cloud-messaging\/."},{"key":"e_1_2_1_7_1","unstructured":"2018. OpenCV library. Retrieved from http:\/\/opencv.org\/.  2018. OpenCV library. Retrieved from http:\/\/opencv.org\/."},{"key":"e_1_2_1_8_1","volume-title":"Rodrigo Benenson, Mario Fritz","author":"Aditya Paarijaat","year":"2016"},{"key":"e_1_2_1_9_1","volume-title":"RSA Conference. Springer.","author":"Aguilar-Melchor Carlos","year":"2016"},{"key":"e_1_2_1_10_1","volume-title":"Annual Cryptology Conference. Springer.","author":"Brakerski Zvika","year":"2011"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2014.2350431"},{"key":"e_1_2_1_12_1","first-page":"397","article-title":"Efficient k-out-of-n oblivious transfer schemes","volume":"14","author":"Chu Cheng-Kang","year":"2008","journal-title":"Journal of Universal Computer Science"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS.2014.6996249"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2014.2385097"},{"key":"e_1_2_1_15_1","unstructured":"Itay Hubara Matthieu Courbariaux Daniel Soudry Ran El-Yaniv and Yoshua Bengio. 2016. Binarized neural networks. In Advances in Neural Information Processing Systems.  Itay Hubara Matthieu Courbariaux Daniel Soudry Ran El-Yaniv and Yoshua Bengio. 2016. Binarized neural networks. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_1_16_1","volume-title":"ACM International Conference on Multimedia.","author":"Jia Yangqing","year":"2014"},{"key":"e_1_2_1_17_1","volume-title":"IEEE International Conference on Automatic Face 8 Gesture Recognition.","author":"Jiang Huaizu","year":"2017"},{"key":"e_1_2_1_18_1","volume-title":"ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN).","author":"Jiang Yurong","year":"2013"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2017.8228621"},{"key":"e_1_2_1_20_1","volume-title":"IEEE Conference on Communications and Network Security (CNS).","author":"Khazbak Youssef","year":"2018"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the International Conference on Internet of Things Design and Implementation (IoTDI).","author":"Khazbak Youssef","year":"2019"},{"key":"e_1_2_1_22_1","volume-title":"Advances in Face Detection and Facial Image Analysis","author":"Learned-Miller Erik"},{"key":"e_1_2_1_23_1","volume-title":"IEEE International Conference on Distributed Computing Systems (ICDCS).","author":"Li Qinghua","year":"2014"},{"key":"e_1_2_1_24_1","volume-title":"Annual International Conference on the Theory and Applications of Cryptographic Techniques. Springer.","author":"Lyubashevsky Vadim","year":"2010"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7026220"},{"key":"e_1_2_1_26_1","volume-title":"ACM Conference on Embedded Networked Sensor Systems (SenSys).","author":"Miluzzo Emiliano"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.41"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2647223"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.376"},{"key":"e_1_2_1_30_1","volume-title":"Kestrel: Video analytics for augmented multi-camera vehicle tracking","author":"Qiu Hang","year":"2018"},{"key":"e_1_2_1_31_1","volume-title":"Protecting Privacy in Video Surveillance","author":"Schiff Jeremy"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_2_1_33_1","volume-title":"Chiao Fe Shu, and Max Lu","author":"Senior Andrew","year":"2005"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2462456.2464440"},{"key":"e_1_2_1_35_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2015.2495291"},{"key":"e_1_2_1_37_1","volume-title":"Branchynet: Fast inference via early exiting from deep neural networks","author":"Teerapittayanon Surat","year":"2016"},{"key":"e_1_2_1_38_1","first-page":"934","article-title":"Differentially private location protection for worker datasets in spatial crowdsourcing","volume":"16","author":"To Hien","year":"2016","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_2_1_39_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition.","author":"Matthew"},{"key":"e_1_2_1_40_1","volume-title":"ACM International Conference on Multimedia Systems (MMsys).","author":"Wang Junjue","year":"2017"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2833032"},{"key":"e_1_2_1_42_1","volume-title":"ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN).","author":"Wu Yibo","year":"2017"},{"key":"e_1_2_1_43_1","volume-title":"Photo crowdsourcing for area coverage in resource constrained environments","author":"Wu Yibo"},{"key":"e_1_2_1_44_1","first-page":"1249","article-title":"Smartphoto: A resource-aware crowdsourcing approach for image sensing with smartphones","volume":"15","author":"Wu Yibo","year":"2015","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_2_1_46_1","volume-title":"IEEE Conference on Communications and Network Security (CNS).","author":"Ziad M. Tarek Ibn","year":"2016"}],"container-title":["ACM Transactions on Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3375878","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3375878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:15Z","timestamp":1750199895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3375878"}},"subtitle":["A Privacy Preserving System for Locating Targets through IoT Cameras"],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3375878"],"URL":"https:\/\/doi.org\/10.1145\/3375878","relation":{},"ISSN":["2691-1914","2577-6207"],"issn-type":[{"value":"2691-1914","type":"print"},{"value":"2577-6207","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6]]},"assertion":[{"value":"2019-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}