• 한국소음진동공학회논문집
• /
• 제24권2호
• /
• pp.146-153
• /
• 2014

This study investigates the effects of seat-design elements such as seating arrangement, shape, and height on speech privacy in high-speed trains. For the evaluation of speech privacy, acoustic simulation software was used to reproduce room acoustical conditions in passenger cars on the basis of in-situ measurement data. The influences of speech source directivity and source height on privacy distance ($r_P$) were investigated, and it was found that $r_P$ determined using an omni-directional source was relatively shorter than that determined using a directional source. It was also found that $r_P$ decreased when the source height was lower than the height of the seat-back because the seat-back blocked the propagation of speech from the sound source. The effect of seating arrangement was not significant when comparing the vis-a-vis seating and one-side seating arrangements. In addition, among the alternative seat-designs, the seats that block the space between the seats and cover the space near the ear were found to show significantly enhanced speech privacy in high-speed train passenger cars.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2014년도 춘계학술대회 논문집
• /
• pp.358-359
• /
• 2014

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2014년도 춘계학술대회 논문집
• /
• pp.360-361
• /
• 2014

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2013년도 춘계학술대회 논문집
• /
• pp.602-603
• /
• 2013

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2013년도 추계학술대회 논문집
• /
• pp.434-435
• /
• 2013

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권6호
• /
• pp.2612-2633
• /
• 2020

Due to the explosive growth of multimedia speech data, how to protect the privacy of speech data and how to efficiently retrieve speech data have become a hot spot for researchers in recent years. In this paper, we proposed an encrypted speech retrieval scheme based on long short-term memory (LSTM) neural network and deep hashing. This scheme not only achieves efficient retrieval of massive speech in cloud environment, but also effectively avoids the risk of sensitive information leakage. Firstly, a novel speech encryption algorithm based on 4D quadratic autonomous hyperchaotic system is proposed to realize the privacy and security of speech data in the cloud. Secondly, the integrated LSTM network model and deep hashing algorithm are used to extract high-level features of speech data. It is used to solve the high dimensional and temporality problems of speech data, and increase the retrieval efficiency and retrieval accuracy of the proposed scheme. Finally, the normalized Hamming distance algorithm is used to achieve matching. Compared with the existing algorithms, the proposed scheme has good discrimination and robustness and it has high recall, precision and retrieval efficiency under various content preserving operations. Meanwhile, the proposed speech encryption algorithm has high key space and can effectively resist exhaustive attacks.

• 한국정보기술학회 영문논문지
• /
• 제10권2호
• /
• pp.49-59
• /
• 2020

As technologies have been developing, our lives are getting easier. Today we are surrounded by the new technologies such as AI and IoT. Moreover, the word, "smart" is a very broad one because we are trying to change our daily environment into smart one by using those technologies. For example, the traditional workplaces have changed into smart offices. Since the 3rd industrial revolution, we have used the touch interface to operate the machines. In the 4th industrial revolution, however, we are trying adding the speech recognition module to the machines to operate them by giving voice commands. Today many of the things are communicated with human by voice commands. Many of them are called AI things and they do tasks which users request and do tasks more than what users request. In the 4th industrial revolution, we use smartphones all the time every day from the morning to the night. For this reason, the privacy using phone is not guaranteed sometimes. For example, the caller's voice can be heard through the phone speaker when accepting a call. So, it is needed to protect privacy on smartphone and it should work automatically according to the user context. In this aspect, this paper proposes a method to adjust the voice volume for call to protect privacy on smartphone according to the user context.

• 한국음향학회지
• /
• 제38권4호
• /
• pp.396-405
• /
• 2019

본 논문에서는 대학 내 밀폐형 사무실의 SPC(Speech Privacy Class) 값을 측정하고 평가하였다. 대학 캠퍼스 내 3곳의 건물에 위치한 5곳 대상공간에서 실간 음압레벨차이(Level Difference, LD)와 수음실의 암소음 레벨($L_b$)을 각각 측정하였다. 5곳 대상공간은 모두 인접실과 복도가 인접해있다. SPC값을 도출하기 위해 필요한 LD값과 기존의 차음성능 측정방법인 투과손실(Transmission Loss, TL)을 함께 측정하여 비교하였다. 측정결과, 5곳 대상공간은 SPC 최소 기준치인 70을 만족하지 못하였다. 5곳 대상공간의 평균 $L_b$값은 29.2 dB이며 SPC 최소 기준치를 만족하기 위해서는 LD값이 41 dB 이상이어야 한다. SPC 최소 기준치를 만족하기 위해서 1/3옥타브밴드 160 Hz ~ 5000 Hz 주파수대역에서 평균 TL값은 40 dB 이상이 되도록 음향설계가 이루어져야 한다. LD값에 가장 큰 영향을 미치는 인자는 음원실과 수음실 간 인접벽체의 개구부 유무이다. 따라서 인접벽체에 개구부가 존재할 경우 차음성능이 높은 재료로 개구부를 대체하여 적절한 SPC값을 만족할 수 있다.

• International Journal of Computer Science & Network Security
• /
• 제21권6호
• /
• pp.137-142
• /
• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2010년도 추계학술대회 논문집
• /
• pp.283-284
• /
• 2010