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Model Contrastive Federated Learning on Re-Identification
Seongyoon Kim, Woojin Chung, Sungwoo Cho, Yongjin Yang, Shinhyeok Hwang, Se-Young Yun
http://doi.org/10.5626/JOK.2024.51.9.841
Advances in data collection and computing power have dramatically increased the integration of AI technology into various services. Traditional centralized cloud data processing raises concerns over the exposure of sensitive user data. To address these issues, federated learning (FL) has emerged as a decentralized training method where clients train models locally on their data and send locally updated models to a central server. The central server aggregates these locally updated models to improve a global model without directly accessing local data, thereby enhancing data privacy. This paper presents FedCON, a novel FL framework specifically designed for re-identification (Re-ID) tasks across various domains. FedCON integrates contrastive learning with FL to enhance feature representation, which is crucial for Re-ID tasks that emphasize similarity between feature vectors to match identities across different images. By focusing on feature similarity, FedCON can effectively addresses data heterogeneity challenges and improve the global model's performance in Re-ID applications. Empirical studies on person and vehicle Re-ID datasets demonstrated that FedCON outperformed existing FL methods for Re-ID. Our experiments with FedCON on various CCTV datasets for person Re-ID showed superior performance to several baselines. Additionally, FedCON significantly enhanced vehicle Re-ID performance on real-world datasets such as VeRi-776 and VRIC, demonstrating its practical applicability.
Privacy-Preserving Data Publishing: Research on Trends in De-identification Techniques for Structured and Unstructured Data
Yongki Hong, Gihyuk Ko, Heedong Yang, Seung Hwan Ryu
http://doi.org/10.5626/JOK.2023.50.11.1008
The advent of AI has seen an increased demand for data for AI development, leading to a proliferation of data sharing and distribution. However, there is also the risk of personal information disclosure during data utilization and therefore, it is necessary to undergo a process of de-identification before distributing the data. Privacy-Preserving Data Publishing (PPDP) is a series of procedures aimed at adhering to specified privacy guidelines while maximizing the utility of data. It has been continuously researched and developed. Since the early 2000s, techniques for de-identifying structured data (e.g., tables or relational data) were studied. As a significant portion of the collected data is now unstructured data and its proportion is increasing, research on de-identification techniques for unstructured data is also actively being conducted. In this paper, we aim to introduce the existing de-identification techniques for structured data and discuss recent trends in de-identification techniques for unstructured data.
Network-level Tracker Detection Using Features of Encrypted Traffic
Dongkeun Lee, Minwoo Joo, Wonjun Lee
http://doi.org/10.5626/JOK.2022.49.4.314
Third-party trackers breach users’ data privacy by compiling large amounts of personal data such as location or browsing history through web tracking techniques. Although previous research has proposed several methods to protect the users from web tracking via its detection and blockage, their effectiveness is limited in terms of dependency or performance. To this end, this paper proposes a novel approach to detect trackers at the network level using features of encrypted traffic. The proposed method first builds a classification model based on the features extracted from side-channel information of encrypted traffic generated by trackers. It then prevents leakage of user information by accurately detecting tracker traffic within the network independently from the user’s browsers or devices. We validate the feasibility of utilizing features of encrypted traffic in tracker detection by studying the distinctive characteristics of tracker traffic derived from real-world encrypted traffic analysis.
Time-series Location Data Collection and Analysis Under Local Differential Privacy
Kijung Jung, Hyukki Lee, Yon Dohn Chung
http://doi.org/10.5626/JOK.2022.49.4.305
As the prevalence of smart devices that can generate location data, the number of location-based services is exploding. Since the user’s location data are sensitive information, if the original data are utilized in their original form, the privacy of individuals could be breached. In this study, we proposed a time-series location data collection and analysis method that satisfies local differential privacy, which is a strong privacy model for the data collection environment and considers the characteristics of time-series location data. In the data collection process, the location of an individual is expressed as a bit array. After that, each bit of the array is perturbed by randomized responses for privacy preservation. In the data analysis process, we analyzed the location frequency using hidden Markov model. Moreover, we performed additional spatiotemporal correlation analysis, which is not possible in the existing analysis methods. To demonstrate the performance of the proposed method, we generated trajectory data based on the Seoul subway and analyzed the results of our method.
Research on WGAN models with Rényi Differential Privacy
Sujin Lee, Cheolhee Park, Dowon Hong, Jae-kum Kim
http://doi.org/10.5626/JOK.2021.48.1.128
Personal data is collected through various services and managers extract values from the collected data and provide individually customized services by analyzing the results. However, data that contains sensitive information, such as medical data, must be protected from privacy breaches. Accordingly, to mitigate privacy invasion, Generative Adversarial Network(GAN) is widely used as a model for generating synthetic data. Still, privacy vulnerabilities exist because GAN models can learn not only the characteristics of the original data but also the sensitive information contained in the original data. Hence, many studies have been conducted to protect the privacy of GAN models. In particular, research has been actively conducted in the field of differential privacy, which is a strict privacy notion. But it is insufficient to apply it to real environments in terms of the usefulness of the data. In this paper, we studied GAN models with Rényi differential privacy, which preserve the utility of the original data while ensuring privacy protection. Specifically, we focused on WGAN and WGAN-GP models, compared synthetic data generated from non-private and differentially private models, and analyzed data utility in each scenario.
Models for Privacy-preserving Data Publishing : A Survey
Jongseon Kim, Kijung Jung, Hyukki Lee, Soohyung Kim, Jong Wook Kim, Yon Dohn Chung
In recent years, data are actively exploited in various fields. Hence, there is a strong demand for sharing and publishing data. However, sensitive information regarding people can breach the privacy of an individual. To publish data while protecting an individual’s privacy with minimal information distortion, the privacy- preserving data publishing(PPDP) has been explored. PPDP assumes various attacker models and has been developed according to privacy models which are principles to protect against privacy breaching attacks. In this paper, we first present the concept of privacy breaching attacks. Subsequently, we classify the privacy models according to the privacy breaching attacks. We further clarify the differences and requirements of each privacy model.
A Spatial Transformation Scheme Supporting Data Privacy and Query Integrity for Outsourced Databases
Hyeong-Il Kim, Young-Ho Song, Jaewoo Chang
Due to the popularity of location-based services, the amount of generated spatial data in daily life has been dramatically increasing. Therefore, spatial database outsourcing has become popular for data owners to reduce the spatial database management cost. The most important consideration in database outsourcing is meeting the privacy requirements and guarantying the integrity of the query result. However, most of existing database transformation techniques do not support both of the data privacy and integrity of the query result. To solve this problem, we propose a spatial data transformation scheme that utilizes the shearing transformation with rotation shifting. In addition, we described the attack models to measure the data privacy of database transformation schemes. Finally, we demonstrated through the experimental evaluations that our scheme provides high level of data protection against different kinds of attack models, compared to the existing schemes, while guaranteeing the integrity of the query result sets.
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