• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.2
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• pp.146-153
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• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.358-359
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.360-361
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.602-603
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.434-435
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• 2013

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2612-2633
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• 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.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.49-59
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• 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.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.396-405
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• 2019

The speech privacy of closed office rooms located in a university campus was measured and assessed in terms of SPC (Speech Privacy Class) values. The measurements of two quantities, the LD (Level Difference) between a source and a receiving room, and the background noise level ($L_b$) at the receiving room were carried out in 5 rooms located in 3 different buildings in the university campus. Each of the 5 rooms was adjacent to both offices and corridors through walls. The TL (Transmission Loss) between the source and the receiver room was also measured to compare the difference of two standard methods, ASTM E2836-10 and KS F 2809. The present results show that the speech privacy of the 5 office rooms is not met the requirement for a minimum SPC values of 70. A minimum LD value of 41 dB between the source and the receiver room should be achieved for having a SPC value of 70 when the mean measured value of $L_b$ at the receiving room is 29.2 dB. That is, the TL(avg) value averaged over the octave bands from 160 Hz to 5000 Hz between the source and the receiver room should be or greater than 40 dB. The most important architectural factor influencing the LD value is the presence of openings, such as doors, and windows, on the adjacent walls between the source and receiving room. Therefore, if the opening of the adjacent wall is replaced by an opening with high sound insulation, the appropriate SPC value of the research and office rooms can be achieved.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.137-142
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• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.283-284
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• 2010