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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.
A Secure and Practical Encrypted Data De-duplication with Proof of Ownership in Cloud Storage
Cheolhee Park, Dowon Hong, Changho Seo
In cloud storage environment, deduplication enables efficient use of the storage. Also, in order to save network bandwidth, cloud storage service provider has introduced client-side deduplication. Cloud storage service users want to upload encrypted data to ensure confidentiality. However, common encryption method cannot be combined with deduplication, because each user uses a different private key. Also, client-side deduplication can be vulnerable to security threats because file tag replaces the entire file. Recently, proof of ownership schemes have suggested to remedy the vulnerabilities of client-side deduplication. Nevertheless, client-side deduplication over encrypted data still causes problems in efficiency and security. In this paper, we propose a secure and practical client-side encrypted data deduplication scheme that has resilience to brute force attack and performs proof of ownership over encrypted data.
Encrypted Data Deduplication Using Key Issuing Server
Hyun-il Kim, Cheolhee Park, Dowon Hong, Changho Seo
Data deduplication is an important technique for cloud storage savings. These techniques are especially important for encrypted data because data deduplication over plaintext is basically vulnerable for data confidentiality. We examined encrypted data deduplication with the aid of a key issuing server and compared Convergent Encryption with a technique created by M.Bellare et al. In addition, we implemented this technique over not only Dropbox but also an open cloud storage service, Openstack Swift. We measured the performance for this technique over Dropbox and Openstack Swift. According to our results, we verified that the encrypted data deduplication technique with the aid of a key issuing server is a feasible and versatile method.
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