• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.672-675
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• 2012

음성은 가장 익숙하고 편리한 의사 소통 수단으로 스마트폰과 같이 크기가 작은 모바일 기기의 입력 인터페이스로 적합하다. 서버 기반의 음성 인식은 서버를 방문하는 다양한 사용자들을 대상으로 음성 모델을 구축하기 때문에 음성 인식률을 높일 수 있고 상용화가 가능하다. 구글 음성인식, 아이폰의 시리(SiRi)가 대표적인 예이며 최근 스마트폰 사용자의 증가로 이에 대한 수요가 급증하고 있다. 서버 기반 음성 인식 기법에서 음성 인식은 스마트폰과 인터넷으로 연결되어 있는 원격지 서버에서 이루어진다. 따라서, 사용자는 스마트폰에 저장된 음성 데이터를 인터넷을 통하여 음성 인식 서버로 전달해야 된다[1, 2]. 음성 데이터는 사용자 고유 정보를 가지고 있으므로 개인 인증 및 식별을 위한 용도로 사용될 수 있으며 음성의 톤, 음성 신호의 피치, 빠르기 등을 통해서 사용자의 감정까지도 판단 할 수 있다[3]. 서버 기반 음성 인식에서 네트워크로 전송되는 사용자 음성 데이터는 제 3 자에게 쉽게 노출되기 때문에 화자의 신분 및 감정이 알려지게 되어 프라이버시 침해를 받게 된다. 본 논문에서는 화자의 프라이버시를 보호하기 위하여 사용자 음성 데이터로부터 개인의 고유 특징 및 현재 상태를 파악할 수 있는 감정 정보를 제거하는 기법을 설계 및 구현하였다.

• Informatization Policy
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• v.30 no.2
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• pp.22-45
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• 2023

In research on the use of AI-based voice assistant services, problems related to the user's trust and privacy protection arising from the experience of service use are constantly being raised. The purpose of this study was to investigate empirically the effects of individual trust in AI and online privacy concerns on the continued use of AI-based voice assistants, specifically the impact of their interaction. In this study, question items were constructed based on previous studies, with an online survey conducted among 405 respondents. The effect of the user's trust in AI and privacy concerns on the adoption and continuous use intention of AI-based voice assistant services was analyzed using the Heckman selection model. As the main findings of the study, first, AI-based voice assistant service usage behavior was positively influenced by factors that promote technology acceptance, such as perceived usefulness, perceived ease of use, and social influence. Second, trust in AI had no statistically significant effect on AI-based voice assistant service usage behavior but had a positive effect on continuous use intention. Third, the privacy concern level was confirmed to have the effect of suppressing continuous use intention through interaction with trust in AI. These research results suggest the need to strengthen user experience through user opinion collection and action to improve trust in technology and alleviate users' concerns about privacy as governance for realizing digital government. When introducing artificial intelligence-based policy services, it is necessary to disclose transparently the scope of application of artificial intelligence technology through a public deliberation process, and the development of a system that can track and evaluate privacy issues ex-post and an algorithm that considers privacy protection is required.

• Journal of Advanced Navigation Technology
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• v.15 no.3
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• pp.378-385
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• 2011

This paper introduces technical aspects of the privacy exposure problem of the video and the audio information on the personal computer and proposes a countermeasure to them. According to the increased number of peripherals for computers, especially including the cameras and the mikes, it is required to be careful on the privacy exposure. Currently, some incorporated or standalone cameras have a pilot lamp to indicate their usage. However, many other cameras and all mikes have not equipped with the pilot lamp or other dedicated indicator. Even though this problem doesn't obstruct their assigned functionalities, it should make the devices susceptible to be exposed with the information they are gathering without any notice to the owners. As a countermeasure to the problem, this paper proposes a reasonable solution that alarms the access trials to the devices and implements programs for the practical sniffing and its counterpart.

• Journal of KSNVE
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• v.27 no.2
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• pp.22-25
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• 2017

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.660-670
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• 2021

As various people use the cafe for various purposes, it is difficult to hear conversations with the accompanying people due to the noise and background music of people around the respondents. In addition, there is a need for improvement related to the noise and sound inside the cafe, such as making it easier to hear the conversations of nearby users. 212 adult men and women participated in the questionnaire on the survey on cafe acoustics and noise conditions. As a result of the survey, about two-thirds of the respondents said that they did not prefer noisy cafes, and that the noise of cafes had a negative effect. The major source of noise in cafes is the sound of people around users, and more than 40 % of the respondents said that they could not hear well the sound of conversations with their accompanying people due to the sounds of those around them, or that they were concerned about their own conversations being transmitted to those around them. As a result of the survey on cafe sound and noise, it was found that improvements were needed to secure the voice privacy of cafe users as well as the voice intelligibility.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.560-567
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• 2013

The release of iPhone by Apple in 2009 has changed the life pattern of an individual tremendously. That is, with the emergence of a smart phone, various services including voice/video call, camera, receiving and sending of e-mail, and web browsing have been realized. However, the broader the scope of the use of a smart phone has become, the greater the need for companies to introduce an MDM solution for protecting important documents has become. However the MDM solution may have a problem in that all data such as contacts, pictures, and memos saved in the smart phone can be accessed unlimitedly. For this reason, there is a risk that unwanted violation of privacy may happen to smart phone users. This paper proposed a plan to protect a personal privacy file of smart phone users, which disables access by others except for related smart phone users by enabling a person in charge of security or an MDM manager in a company to have access only to the file which was allowed by smart phone users to be disclosed and by saving non-disclosed files in a virtual disk.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.631-634
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• 2006

정보화 사회로 변하고 있는 지금 통신기술 역시 발전하고 있으며 음성통신 및 비음성통신(전자 매체를 통한 통신)의 활용도가 점점 증가하고 있고, 사용되는 정보의 양 역시 늘어나고 있다. 그러나 정보의 흐름이 대량화, 가속화됨에 따라 통신매체를 통해 각종 개인정보가 노출될 위험이 증가하고 있으며 이에 따라 개인의 프라이버시 역시 위협받고 있다. 본고에서는 통신기술의 발달과 개인의 통신비밀 보호를 어떻게 조화시킬 수 있는가를 미국의 사례를 통하여 알아보겠다.

• 정보보호뉴스
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• s.138
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• pp.58-59
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• 2009

사회가 발전함에 따라 개인정보의 의미는 인격권과 재산권이 혼재된 새로운 의미로 그 범위가 확대되고 있다. 개인정보의 범위에는 SMS, 음성통화 내역, 사진, 영상 등 개인을 식별해 프라이버시를 침해할 수 있는 정보를 비롯해 마케팅에 활용 가능한 주민등록번호, 계좌번호, 카드번호, 거래내역, 신용정보 즉, 경제적인 효용(편익)을 얻기 위해 기업에게 제공하는 식별정보 등이 모두 개인정보에 포함된다. 최근 발생한 일련의 개인정보 유출사고와 법적 공방은 개인과 관련된 다양한 정보를 보호하기 위해 기업이 추가적인 조치가 필요하다는 것을 의미한다. 하지만 기업이 개인정보를 보호하기 위해서는 기존 방화벽으로 대표되는 외부와 내부를 차단하는 보안에 대한 관점 및 체계로는 한계를 지닌다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.111-118
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• 2022

In recent years, the number of single-person elderly households has increased, but when an emergency situation occurs inside the house in the case of single-person households, it is difficult to inform the outside world. Various smart home solutions have been proposed to detect emergency situations in single-person households, but it is difficult to use video media such as home CCTV, which has problems in the privacy area. Furthermore, if only a single sensor is used to analyze the abnormal situation of the elderly in the house, accurate situational analysis is limited due to the constraint of data amount. In this paper, therefore, we propose an algorithm of abnormal situation detection fusion inside the house by fusing 2DLiDAR, dust, and voice sensors, which are closely related to everyday life while protecting privacy, based on their correlations. Moreover, this paper proves the algorithm's reliability through data collected in a real-world environment. Adnormal situations that are detectable and undetectable by the proposed algorithm are presented. This study focuses on the detection of adnormal situations in the house and will be helpful in the lives of single-household users.