Visible to the public Biblio

Filters: Author is Hur, Junbeom  [Clear All Filters]
2019-03-18
Hong, Younggee, Kwon, Hyunsoo, Lee, Jihwan, Hur, Junbeom.  2018.  A Practical De-mixing Algorithm for Bitcoin Mixing Services. Proceedings of the 2Nd ACM Workshop on Blockchains, Cryptocurrencies, and Contracts. :15–20.
Bitcoin mixing services improve anonymity by breaking the connection between Bitcoin addresses. In the darkweb environment, many illegal trades, such as in drugs or child pornography, avoid their transactions being traced by exploiting mixing services. Therefore, de-mixing algorithms are needed to identify illegal financial flows and to reduce criminal activity. Unfortunately, to the best of our knowledge, few studies on analyzing mixing services and de-anonymizing transactions have been proposed. In this paper, we conduct an in-depth analysis of real-world mixing services, and propose a de-mixing algorithm for Helix, one of the most widely used Bitcoin mixing services. The proposed algorithm de-anonymizes the relationship between the input and output addresses of mixing services by exploiting the static and dynamic parameters of mixing services. Our experiment showed that, we could identify the relationships between the input and output addresses of the Helix mixing service with a 99.14% accuracy rate.
2018-01-16
Shin, Youngjoo, Koo, Dongyoung, Hur, Junbeom.  2017.  A Survey of Secure Data Deduplication Schemes for Cloud Storage Systems. ACM Comput. Surv.. 49:74:1–74:38.

Data deduplication has attracted many cloud service providers (CSPs) as a way to reduce storage costs. Even though the general deduplication approach has been increasingly accepted, it comes with many security and privacy problems due to the outsourced data delivery models of cloud storage. To deal with specific security and privacy issues, secure deduplication techniques have been proposed for cloud data, leading to a diverse range of solutions and trade-offs. Hence, in this article, we discuss ongoing research on secure deduplication for cloud data in consideration of the attack scenarios exploited most widely in cloud storage. On the basis of classification of deduplication system, we explore security risks and attack scenarios from both inside and outside adversaries. We then describe state-of-the-art secure deduplication techniques for each approach that deal with different security issues under specific or combined threat models, which include both cryptographic and protocol solutions. We discuss and compare each scheme in terms of security and efficiency specific to different security goals. Finally, we identify and discuss unresolved issues and further research challenges for secure deduplication in cloud storage.