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Journal articles on the topic 'Privacy preserving recognition'

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Author: Grafiati

Published: 14 March 2023

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1

Chamikara, M. A. P., P. Bertok, I. Khalil, D. Liu, and S. Camtepe. "Privacy Preserving Face Recognition Utilizing Differential Privacy." Computers & Security 97 (October 2020): 101951. http://dx.doi.org/10.1016/j.cose.2020.101951.

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2

Li, Yuancheng, Yimeng Wang, and Daoxing Li. "Privacy-preserving lightweight face recognition." Neurocomputing 363 (October 2019): 212–22. http://dx.doi.org/10.1016/j.neucom.2019.07.039.

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3

Woubie, Abraham, and Tom Backstrom. "Federated Learning for Privacy-Preserving Speaker Recognition." IEEE Access 9 (2021): 149477–85. http://dx.doi.org/10.1109/access.2021.3124029.

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4

Xiang, Can, Chunming Tang, Yunlu Cai, and Qiuxia Xu. "Privacy-preserving face recognition with outsourced computation." Soft Computing 20, no. 9 (July 8, 2015): 3735–44. http://dx.doi.org/10.1007/s00500-015-1759-5.

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5

Xu, Honghui, Zhipeng Cai, and Wei Li. "Privacy-Preserving Mechanisms for Multi-Label Image Recognition." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (August 31, 2022): 1–21. http://dx.doi.org/10.1145/3491231.

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Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.

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6

Wang, Lin, Chuan Zhao, Kun Zhao, Bo Zhang, Shan Jing, Zhenxiang Chen, and Kuiheng Sun. "Privacy-Preserving Collaborative Computation for Human Activity Recognition." Security and Communication Networks 2022 (February 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/9428610.

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Human Activity Recognition (HAR) enables computer systems to assist users with their tasks and improve their quality of life in rehabilitation, daily life tracking, fitness, and cognitive disorder therapy. It is a hot topic in the field of machine learning, and HAR is gaining more attention among researchers due to its unique societal and economic advantages. This paper focuses on a collaborative computation scenario where a group of participants will securely and collaboratively train an accurate HAR model. The training process requires collecting a massive number of personal activity features and labels, which raises privacy problems. We decentralize the training process locally to each client in order to ensure the privacy of training data. Furthermore, we use an advanced secure aggregation algorithm to ensure that malicious participants cannot extract private information from the updated parameters even during the aggregation phase. Edge computing nodes have been introduced into our system to address the problem of data generation devices’ insufficient computing power. We replace the traditional central server with smart contract to make the system more robust and secure. We achieve the verifiability of the packaged nodes using the publicly auditability feature of blockchain. According to the experimental data, the accuracy of the HAR model trained by our proposed framework reaches 93.24%, which meets the applicability requirements. The use of secure multiparty computation techniques unavoidably increases training time, and experimental results show that a round of iterations takes 36.4 seconds to execute, which is still acceptable.

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Kumar, Kambala Vijaya, and Jonnadula Harikiran. "Privacy preserving human activity recognition framework using an optimized prediction algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 254. http://dx.doi.org/10.11591/ijai.v11.i1.pp254-264.

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Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.

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8

Wang, Yinggui, Jian Liu, Man Luo, Le Yang, and Li Wang. "Privacy-Preserving Face Recognition in the Frequency Domain." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2558–66. http://dx.doi.org/10.1609/aaai.v36i3.20157.

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Some applications may require performing face recognition (FR) on third-party servers, which could be accessed by attackers with malicious intents to compromise the privacy of users’ face information. This paper advocates a practical privacy-preserving FR scheme without key management realized in the frequency domain. The new scheme first collects the components of the same frequency from different blocks of a face image to form component channels. Only part of the channels are retained and fed into the analysis network that performs an interpretable privacy-accuracy trade-off analysis to identify channels important for face image visualization but not crucial for maintaining high FR accuracy. For this purpose, the loss function of the analysis network consists of the empirical FR error loss and a face visualization penalty term, and the network is trained in an end-to-end manner. We find that with the developed analysis network, more than 94% of the image energy can be dropped while the face recognition accuracy stays almost undegraded. In order to further protect the remaining frequency components, we propose a fast masking method. Effectiveness of the new scheme in removing the visual information of face images while maintaining their distinguishability is validated over several large face datasets. Results show that the proposed scheme achieves a recognition performance and inference time comparable to ArcFace operating on original face images directly.

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9

Zapechnikov, Sergey. "Contemporary trends in privacy-preserving data pattern recognition." Procedia Computer Science 190 (2021): 838–44. http://dx.doi.org/10.1016/j.procs.2021.06.098.

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10

Ma, Zhuo, Yang Liu, Ximeng Liu, Jianfeng Ma, and Kui Ren. "Lightweight Privacy-Preserving Ensemble Classification for Face Recognition." IEEE Internet of Things Journal 6, no. 3 (June 2019): 5778–90. http://dx.doi.org/10.1109/jiot.2019.2905555.

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11

Leong, Shu-Min, Raphaël C. W. Phan, Vishnu Monn Baskaran, and Chee-Pun Ooi. "Privacy-preserving facial recognition based on temporal features." Applied Soft Computing 96 (November 2020): 106662. http://dx.doi.org/10.1016/j.asoc.2020.106662.

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12

Ren, Lijing, and Denghui Zhang. "A Privacy-Preserving Biometric Recognition System with Visual Cryptography." Advances in Multimedia 2022 (March 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/1057114.

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The popularity of more powerful and smarter digital devices has improved the quality of life and poses new challenges to the privacy protection of personal information. In this paper, we propose a biometric recognition system with visual cryptography, which preserves the privacy of biometric features by storing biometric features in separate databases. Visual cryptography combines perfect ciphers and secret sharing in cryptography with images, thus eliminating the complex operations in existing privacy-preserving schemes based on cryptography or watermarking. Since shares do not reveal any feature about biometric information, we can efficiently transmit sensitive information among sensors and smart devices in plain. To abate the influence of noise in visual cryptography, we leverage the generalization ability of transfer learning to train a visual cryptography-based recognition network. Experimental results show that our proposed method keeps the high accuracy of the feature recognition system when providing security.

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13

Ma, Zhuo, Yang Liu, Ximeng Liu, Jianfeng Ma, and Feifei Li. "Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices." IEEE Internet of Things Journal 6, no. 5 (October 2019): 8406–20. http://dx.doi.org/10.1109/jiot.2019.2917933.

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14

Guo, Shangwei, Tao Xiang, and Xiaoguo Li. "Towards efficient privacy-preserving face recognition in the cloud." Signal Processing 164 (November 2019): 320–28. http://dx.doi.org/10.1016/j.sigpro.2019.06.024.

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15

Hashemian, Mina, Farbod Razzazi, Houman Zarrabi, and Mohammad Shahram Moin. "A privacy-preserving distributed transfer learning in activity recognition." Telecommunication Systems 72, no. 1 (February 7, 2019): 69–79. http://dx.doi.org/10.1007/s11235-018-0534-1.

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16

Liu, Kang, Benjamin Tan, and Siddharth Garg. "Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14849–56. http://dx.doi.org/10.1609/aaai.v35i17.17743.

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Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images while maintaining useful information for other tasks. Currently, state-of-the-art approaches use privacy-preserving generative adversarial networks (PP-GANs) for this purpose, for instance, to enable reliable facial expression recognition without leaking users' identity. However, PP-GANs do not offer formal proofs of privacy and instead rely on experimentally measuring information leakage using classification accuracy on the sensitive attributes of deep learning (DL)-based discriminators. In this work, we question the rigor of such checks by subverting existing privacy-preserving GANs for facial expression recognition. We show that it is possible to hide the sensitive identification data in the sanitized output images of such PP-GANs for later extraction, which can even allow for reconstruction of the entire input images, while satisfying privacy checks. We demonstrate our approach via a PP-GAN-based architecture and provide qualitative and quantitative evaluations using two public datasets. Our experimental results raise fundamental questions about the need for more rigorous privacy checks of PP-GANs, and we provide insights into the social impact of these.

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17

Karri, Chiranjeevi, Omar Cheikhrouhou, Ahmed Harbaoui, Atef Zaguia, and Habib Hamam. "Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study." Applied Sciences 11, no. 14 (July 15, 2021): 6522. http://dx.doi.org/10.3390/app11146522.

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Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.

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18

Climent-Pérez, Pau, and Francisco Florez-Revuelta. "Privacy-Preserving Human Action Recognition with a Many-Objective Evolutionary Algorithm." Sensors 22, no. 3 (January 20, 2022): 764. http://dx.doi.org/10.3390/s22030764.

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Wrist-worn devices equipped with accelerometers constitute a non-intrusive way to achieve active and assisted living (AAL) goals, such as automatic journaling for self-reflection, i.e., lifelogging, as well as to provide other services, such as general health and wellbeing monitoring, personal autonomy assessment, among others. Human action recognition (HAR), and in particular, the recognition of activities of daily living (ADLs), can be used for these types of assessment or journaling. In this paper, a many-objective evolutionary algorithm (MaOEA) is used in order to maximise action recognition from individuals while concealing (minimising recognition of) gender and age. To validate the proposed method, the PAAL accelerometer signal ADL dataset (v2.0) is used, which includes data from 52 participants (26 men and 26 women) and 24 activity class labels. The results show a drop in gender and age recognition to 58% (from 89%, a 31% drop), and to 39% (from 83%, a 44% drop), respectively; while action recognition stays closer to the initial value of 68% (from: 87%, i.e., 19% down).

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19

Jung, Im Y. "A review of privacy-preserving human and human activity recognition." International Journal on Smart Sensing and Intelligent Systems 13, no. 1 (2020): 1–13. http://dx.doi.org/10.21307/ijssis-2020-008.

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20

Xiang, Yang, and Kamala Srinivasan. "Privacy preserving existence recognition and construction of hypertree agent organization." Autonomous Agents and Multi-Agent Systems 30, no. 2 (February 11, 2015): 220–58. http://dx.doi.org/10.1007/s10458-015-9285-5.

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21

Novikova, Evgenia, Dmitry Fomichov, Ivan Kholod, and Evgeny Filippov. "Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition." Sensors 22, no. 8 (April 13, 2022): 2983. http://dx.doi.org/10.3390/s22082983.

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Wearable devices and smartphones that are used to monitor the activity and the state of the driver collect a lot of sensitive data such as audio, video, location and even health data. The analysis and processing of such data require observing the strict legal requirements for personal data security and privacy. The federated learning (FL) computation paradigm has been proposed as a privacy-preserving computational model that allows securing the privacy of the data owner. However, it still has no formal proof of privacy guarantees, and recent research showed that the attacks targeted both the model integrity and privacy of the data owners could be performed at all stages of the FL process. This paper focuses on the analysis of the privacy-preserving techniques adopted for FL and presents a comparative review and analysis of their implementations in the open-source FL frameworks. The authors evaluated their impact on the overall training process in terms of global model accuracy, training time and network traffic generated during the training process in order to assess their applicability to driver’s state and behaviour monitoring. As the usage scenario, the authors considered the case of the driver’s activity monitoring using the data from smartphone sensors. The experiments showed that the current implementation of the privacy-preserving techniques in open-source FL frameworks limits the practical application of FL to cross-silo settings.

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22

Kambala, Vijaya Kumar, and Harikiran Jonnadula. "A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework." Bulletin of Electrical Engineering and Informatics 10, no. 6 (December 1, 2021): 3191–201. http://dx.doi.org/10.11591/eei.v10i6.3204.

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There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.

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23

Evtimov, Ivan, Pascal Sturmfels, and Tadayoshi Kohno. "FoggySight: A Scheme for Facial Lookup Privacy." Proceedings on Privacy Enhancing Technologies 2021, no. 3 (April 27, 2021): 204–26. http://dx.doi.org/10.2478/popets-2021-0044.

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Abstract Advances in deep learning algorithms have enabled better-than-human performance on face recognition tasks. In parallel, private companies have been scraping social media and other public websites that tie photos to identities and have built up large databases of labeled face images. Searches in these databases are now being offered as a service to law enforcement and others and carry a multitude of privacy risks for social media users. In this work, we tackle the problem of providing privacy from such face recognition systems. We propose and evaluate FoggySight, a solution that applies lessons learned from the adversarial examples literature to modify facial photos in a privacy-preserving manner before they are uploaded to social media. FoggySight’s core feature is a community protection strategy where users acting as protectors of privacy for others upload decoy photos generated by adversarial machine learning algorithms. We explore different settings for this scheme and find that it does enable protection of facial privacy – including against a facial recognition service with unknown internals.

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Liu, Meng, Hongsheng Hu, Haolong Xiang, Chi Yang, Lingjuan Lyu, and Xuyun Zhang. "Clustering-based Efficient Privacy-preserving Face Recognition Scheme without Compromising Accuracy." ACM Transactions on Sensor Networks 17, no. 3 (June 21, 2021): 1–27. http://dx.doi.org/10.1145/3448414.

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Recently, biometric identification has been extensively used for border control. Some face recognition systems have been designed based on Internet of Things. But the rich personal information contained in face images can cause severe privacy breach and abuse issues during the process of identification if a biometric system has compromised by insiders or external security attacks. Encrypting the query face image is the state-of-the-art solution to protect an individual’s privacy but incurs huge computational cost and poses a big challenge on time-critical identification applications. However, due to their high computational complexity, existing methods fail to handle large-scale biometric repositories where a target face is searched. In this article, we propose an efficient privacy-preserving face recognition scheme based on clustering. Concretely, our approach innovatively matches an encrypted face query against clustered faces in the repository to save computational cost while guaranteeing identification accuracy via a novel multi-matching scheme. To the best of our knowledge, our scheme is the first to reduce the computational complexity from O(M) in existing methods to approximate O (√ M ), where M is the size of a face repository. Extensive experiments on real-world datasets have shown the effectiveness and efficiency of our scheme.

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25

Yadav, Santosh Kumar, Siva Sai, Akshay Gundewar, Heena Rathore, Kamlesh Tiwari, Hari Mohan Pandey, and Mohit Mathur. "CSITime: Privacy-preserving human activity recognition using WiFi channel state information." Neural Networks 146 (February 2022): 11–21. http://dx.doi.org/10.1016/j.neunet.2021.11.011.

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26

Jourdan, Theo, Antoine Boutet, Amine Bahi, and Carole Frindel. "Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring." ACM Transactions on Computing for Healthcare 2, no. 1 (December 30, 2020): 1–22. http://dx.doi.org/10.1145/3416947.

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27

Chen, Chuanming, Wenshi Lin, Shuanggui Zhang, Zitong Ye, Qingying Yu, and Yonglong Luo. "Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation." Intelligent Data Analysis 25, no. 5 (September 15, 2021): 1247–71. http://dx.doi.org/10.3233/ida-205306.

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Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

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28

HONG, TIAN, and WANG XIUKUN. "A NOVEL PRIVACY-PRESERVING ASSOCIATION RULES MINING METHOD." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 06 (September 2010): 995–1009. http://dx.doi.org/10.1142/s021800141000824x.

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In order to mine association rules accurately and efficiently while preserving the privacy thereof, a novel privacy-preserving association rules mining method is proposed in this paper. Known as the partial randomized response based on probability matrix, or PRRPM, this method chooses different data transition strategies to find frequent 1-itemsets and k-itemsets (k > 1). The PRRPM algorithm is explored and its validity examined through theoretical analysis and experiments.

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29

Kandappu, Thivya, Vigneshwaran Subbaraju, and Qianli Xu. "PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–32. http://dx.doi.org/10.1145/3476047.

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Built-in pervasive cameras have become an integral part of mobile/wearable devices and enabled a wide range of ubiquitous applications with their ability to be "always-on". In particular, life-logging has been identified as a means to enhance the quality of life of older adults by allowing them to reminisce about their own life experiences. However, the sensitive images captured by the cameras threaten individuals' right to have private social lives and raise concerns about privacy and security in the physical world. This threat gets worse when image recognition technologies can link images to people, scenes, and objects, hence, implicitly and unexpectedly reveal more sensitive information such as social connections. In this paper, we first examine life-log images obtained from 54 older adults to extract (a) the artifacts or visual cues, and (b) the context of the image that influences an older life-logger's ability to recall the life events associated with a life-log image. We call these artifacts and contextual cues "stimuli". Using the set of stimuli extracted, we then propose a set of obfuscation strategies that naturally balances the trade-off between reminiscability and privacy (revealing social ties) while selectively obfuscating parts of the images. More specifically, our platform yields privacy-utility tradeoff by compromising, on average, modest 13.4% reminiscability scores while significantly improving privacy guarantees -- around 40% error in cloud estimation.

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30

MOHAISEN, Abedelaziz, Nam-Su JHO, Dowon HONG, and DaeHun NYANG. "Privacy Preserving Association Rule Mining Revisited: Privacy Enhancement and Resources Efficiency." IEICE Transactions on Information and Systems E93-D, no. 2 (2010): 315–25. http://dx.doi.org/10.1587/transinf.e93.d.315.

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31

Ullah, Asad, Jing Wang, M. Shahid Anwar, Arshad Ahmad, Shah Nazir, Habib Ullah Khan, and Zesong Fei. "Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition." Security and Communication Networks 2021 (January 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/6673992.

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The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.

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32

Korpela, Joseph, and Takuya Maekawa. "Privacy preserving recognition of object-based activities using near-infrared reflective markers." Personal and Ubiquitous Computing 22, no. 2 (August 14, 2017): 365–77. http://dx.doi.org/10.1007/s00779-017-1070-9.

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33

Ahmad, Fawad, Lee-Ming Cheng, and Asif Khan. "Lightweight and Privacy-Preserving Template Generation for Palm-Vein-Based Human Recognition." IEEE Transactions on Information Forensics and Security 15 (2020): 184–94. http://dx.doi.org/10.1109/tifs.2019.2917156.

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KAJITA, Kaisei, Go OHTAKE, and Kazuto OGAWA. "Privacy-Preserving System for Enriched-Integrated Service." IEICE Transactions on Information and Systems E104.D, no. 5 (May 1, 2021): 647–58. http://dx.doi.org/10.1587/transinf.2020ntp0009.

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35

Pasupuleti, Rajesh, and Narsimha Gugulothu. "PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 5 (June 30, 2013): 3443–51. http://dx.doi.org/10.24297/ijct.v12i5.2914.

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Clustering analysis initiativesÂ a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of theÂ requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected byÂ user.Â In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields goodÂ results in practice with an example ofÂ business data are provided.Â It alsoÂ explains privacy preserving clusters of sensitive data objects.

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Kristiyani, Desi, and Arie Wahyu Wijayanto. "Preserving Women Public Restroom Privacy using Convolutional Neural Networks-Based Automatic Gender Detection." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 31–42. http://dx.doi.org/10.34123/icdsos.v2021i1.29.

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Personal safety and privacy have been the significant concerns among women to use and access public restrooms/toilets, especially in developing countries such as Indonesia. Privacy-enhancing designs are unquestionably expected to ensure no men entering the rooms neither intentionally nor accidentally without prior notice. In this paper, we propose a facial recognition approach to ensure women's safety and privacy in public restroom areas using Convolutional Neural Networks (CNN) model as a gender classifier. Our main contributions are as follows: (1) a webcam feed automatic gender detection model using CNN which may further be connected to a security alarm (2) a publicly available gender-annotated image dataset that embraces Indonesian facial recognition samples. Supplementary Indonesian facial examples are taken from a government-affiliated college, Politeknik Statistika STIS students' photo datasets. The experimental results show a promising accuracy of our proposed model up to 95.84%. This study could be beneficial and useful for wider implementation in supporting the safety system of public universities, offices, and government buildings.

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Kjamilji, Artrim, Arben Idrizi, Shkurte Luma-Osmani, and Ferihane Zenuni-Kjamilji. "Secure Naïve Bayes Classification without Loss of Accuracy with Application to Breast Cancer Prediction." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 397–403. http://dx.doi.org/10.14421/icse.v3.536.

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The classification and prediction accuracy of Machine Learning (ML) algorithms, which often outperform human experts of the related field, have enabled them to be used in areas such as health and disease prediction, image and speech recognition, cyber-security threats and credit-card fraud detection and others. However, laws, ethics and privacy concerns prevent ML algorithms to be used in many real-case scenarios. In order to overcome this problem, we introduce a few flexible and secure building blocks which can be used to build different privacy preserving classifications schemes based on already trained ML models. Then, as a use-case scenario, we utilize and practically use those blocks to enable a privacy preserving Naïve Bayes classifier in the semi-honest model with application to breast cancer detection. Our theoretical analysis and experimental results show that the proposed scheme in many aspects is more efficient in terms of computation and communication cost, as well as in terms of security properties than several state of the art schemes. Furthermore, our privacy preserving scheme shows no loss of accuracy compared to the plain classifier.

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Hou, Ruiqi, Fei Tang, Shikai Liang, and Guowei Ling. "Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment." Security and Communication Networks 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/3630312.

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As a commonly used algorithm in data mining, clustering has been widely applied in many fields, such as machine learning, information retrieval, and pattern recognition. In reality, data to be analyzed are often distributed to multiple parties. Moreover, the rapidly increasing data volume puts heavy computing pressure on data owners. Thus, data owners tend to outsource their own data to cloud servers and obtain data analysis results for the federated data. However, the existing privacy-preserving outsourced k -means schemes cannot verify whether participants share consistent data. Considering the scenarios with multiple data owners and sensitive information security in an outsourced environment, we propose a verifiable privacy-preserving federated k -means clustering scheme. In this article, cloud servers and participants perform k -means clustering algorithm over encrypted data without exposing private data and intermediate results in each iteration. In particular, our scheme can verify the shares from participants when updating the cluster centers based on secret sharing, hash function and blockchain, so that our scheme can resist inconsistent share attacks by malicious participants. Finally, the security and experimental analysis are carried out to show that our scheme can protect private data and get high-accuracy clustering results.

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Kiya, Hitoshi, Teru Nagamori, Shoko Imaizumi, and Sayaka Shiota. "Privacy-Preserving Semantic Segmentation Using Vision Transformer." Journal of Imaging 8, no. 9 (August 30, 2022): 233. http://dx.doi.org/10.3390/jimaging8090233.

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In this paper, we propose a privacy-preserving semantic segmentation method that uses encrypted images and models with the vision transformer (ViT), called the segmentation transformer (SETR). The combined use of encrypted images and SETR allows us not only to apply images without sensitive visual information to SETR as query images but to also maintain the same accuracy as that of using plain images. Previously, privacy-preserving methods with encrypted images for deep neural networks have focused on image classification tasks. In addition, the conventional methods result in a lower accuracy than models trained with plain images due to the influence of image encryption. To overcome these issues, a novel method for privacy-preserving semantic segmentation is proposed by using an embedding that the ViT structure has for the first time. In experiments, the proposed privacy-preserving semantic segmentation was demonstrated to have the same accuracy as that of using plain images under the use of encrypted images.

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Dishabi, Mohammad Reza Ebrahimi, and Mohammad Abdollahi Azgomi. "Differential privacy preserving clustering based on Haar wavelet transform." Intelligent Data Analysis 18, no. 4 (June 27, 2014): 583–608. http://dx.doi.org/10.3233/ida-140659.

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Dimitrievski, Ace, Eftim Zdravevski, Petre Lameski, and Vladimir Trajkovik. "Facilitating privacy-preserving activity recognition in age-friendly environments through low-power devices." Procedia Computer Science 203 (2022): 693–98. http://dx.doi.org/10.1016/j.procs.2022.07.103.

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Wang, Yitu, and Takayuki Nakachi. "A Privacy-Preserving Learning Framework for Face Recognition in Edge and Cloud Networks." IEEE Access 8 (2020): 136056–70. http://dx.doi.org/10.1109/access.2020.3011112.

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Nakamura, Kazuaki, Naoko Nitta, and Noboru Babaguchi. "Encryption-Free Framework of Privacy-Preserving Image Recognition for Photo-Based Information Services." IEEE Transactions on Information Forensics and Security 14, no. 5 (May 2019): 1264–79. http://dx.doi.org/10.1109/tifs.2018.2876752.

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YANG, Xudong, Ling GAO, Yan LI, Jipeng XU, Jie ZHENG, Hai WANG, and Quanli GAO. "A Semantic-Based Dual Location Privacy-Preserving Approach." IEICE Transactions on Information and Systems E105.D, no. 5 (May 1, 2022): 982–95. http://dx.doi.org/10.1587/transinf.2021edp7185.

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Magdy, Safaa, Yasmine Abouelseoud, and Mervat Mikhail. "Privacy preserving search index for image databases based on SURF and order preserving encryption." IET Image Processing 14, no. 5 (April 17, 2020): 874–81. http://dx.doi.org/10.1049/iet-ipr.2019.0575.

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Mtibaa, Aymen, Dijana Petrovska‐Delacrétaz, Jérôme Boudy, and Ahmed Ben Hamida. "Privacy‐preserving speaker verification system based on binary I‐vectors." IET Biometrics 10, no. 3 (February 22, 2021): 233–45. http://dx.doi.org/10.1049/bme2.12013.

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Alrawais, Arwa, Fatemah Alharbi, Moteeb Almoteri, Beshayr Altamimi, Hessa Alnafisah, and Nourah Aljumeiah. "Privacy-Preserving Techniques in Social Distancing Applications: A Comprehensive Survey." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 3 (May 20, 2022): 325–41. http://dx.doi.org/10.20965/jaciii.2022.p0325.

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During the world’s challenge to confront the rapidly spreading coronavirus disease (COVID-19) pandemic and the consequent heavy losses and disruption to society, returning to normal life has become a demand. Social distancing, also known as physical distancing, plays a pivotal role in this scenario. Social distancing is a practice to maintain a safe space between a person and others who are not from the same household, preventing the spread of contagious viral diseases. To support this case, several public authorities and governments around the world have proposed social distancing applications (also known as contact-tracing apps). However, the adoption of these applications is arguable because of concerns regarding privacy and user data protection. In this study, we present a comprehensive survey of privacy-preserving techniques for social distancing applications. We provide an extensive background on social distancing applications, including measuring the physical distance between people. We also discuss various privacy-preserving techniques that are used by social distancing applications; specifically, we thoroughly analyze and compare these applications, considering multiple features. Finally, we provide insights and recommendations for designing social distancing applications while reducing the burden of privacy problems.

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Chandrasekaran, Varun, Chuhan Gao, Brian Tang, Kassem Fawaz, Somesh Jha, and Suman Banerjee. "Face-Off: Adversarial Face Obfuscation." Proceedings on Privacy Enhancing Technologies 2021, no. 2 (January 29, 2021): 369–90. http://dx.doi.org/10.2478/popets-2021-0032.

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Abstract Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences–some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate associated privacy risks result in an imbalanced trade-off between privacy and utility. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to images of the user’s face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.

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Cho, Durkhyun, Jin Han Lee, and Il Hong Suh. "CLEANIR: Controllable Attribute-Preserving Natural Identity Remover." Applied Sciences 10, no. 3 (February 7, 2020): 1120. http://dx.doi.org/10.3390/app10031120.

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We live in an era of privacy concerns. As smart devices such as smartphones, service robots and surveillance cameras spread, preservation of our privacy becomes one of the major concerns in our daily life. Traditionally, the problem was resolved by simple approaches such as image masking or blurring. While these provide effective ways to remove identities from images, there are certain limitations when it comes to a matter of recognition from the processed images. For example, one may want to get ambient information from scenes even when privacy-related information such as facial appearance is removed or changed. To address the issue, our goal in this paper is not only to modify identity from faces but also keeps facial attributes such as color, pose and facial expression for further applications. We propose a novel face de-identification method based on a deep generative model in which we design the output vector from an encoder to be disentangled into two parts: identity-related part and the rest representing facial attributes. We show that by solely modifying the identity-related part from the latent vector, our method effectively modifies the facial identity to a completely new one while the other attributes that are loosely related to personal identity are preserved. To validate the proposed method, we provide results from experiments that measure two different aspects: effectiveness of personal identity modification and facial attribute preservation.

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Deng, Guoqiang, Min Tang, Yuhao Zhang, Ying Huang, and Xuefeng Duan. "Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification." Applied Sciences 12, no. 24 (December 14, 2022): 12873. http://dx.doi.org/10.3390/app122412873.

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Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. Consequently, it is still a huge challenge to train an ANN model in the encrypted-domain. To mitigate this problem, we propose a privacy-preserving ANN system for secure constructing image classifiers, named IPPNN, where the server is able to train an ANN-based classifier on the combined image data of all data owners without being able to observe any images using primitives, such as randomization and functional encryption. Our system achieves faster training time and supports lossless training. Moreover, IPPNN removes the need for multiple communications among data owners and servers. We analyze the security of the protocol and perform experiments on a large scale image recognition task. The results show that the IPPNN is feasible to use in practice while achieving high accuracy.

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