• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.125-137
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• 2007

This paper proposes a new scheme to provide the location privacy of sources in Wireless Sensor Networks (WSNs). Because the geographical location of a source sensor reveals contextual information on an 'event' in WSN, anonymizing the source location is an important issue. Despite abundant research efforts, however, about data confidentiality and authentication in WSN, privacy issues have not been researched well so far. Moreover, many schemes providing the anonymity of communication parties in Internet and Ad-hoc networks are not appropriate for WSN environments where sensors are very resource limited and messages are forwarded in a hop-by-hop manner through wireless channel. In this paper, we first categorize the type of eavesdroppers for WSN as Global Eavesdropper and Compromising Eavesdropper. Then we propose a novel scheme which provides the anonymity of a source according to the types of eavesdroppers. Furthermore, we analyze the degree of anonymity of WSN using the entropy-based modeling method. As a result, we show that the proposed scheme improves the degree of anonymity compared to a method without any provision of anonymity and also show that the transmission range plays a key role to hide the location of source sensors.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.805-816
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• 2017

Tor is a popular, low-latency open network that offers online anonymity to users by concealing their information from anyone conducting traffic analysis. At the same time, a number of conventional passive and active attacking schemes have been proposed to compromise the anonymity provided by the Tor network. In addition to attacks on the network through traffic analysis, interacting with an unsecured application can reveal a Tor user's IP address. Specific traffic from such applications bypasses Tor proxy settings in the user's machine and forms connections outside the Tor network. This paper presents such applications and shows how they can be used to deanonymize Tor users. Extensive test studies performed in the paper show that applications such as Flash and BitTorrent can reveal the IP addresses of Tor users.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.