Papers - Nishi, Hiroaki

Greenhouse Heat Map Generation with Deep Neural Network Using Limited Number of Temperature Sensors.

Ayu Sonoda, Yuki Takayama, Ayaki Sugawara, Hiroaki Nishi

IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society(IECON) (IEEE)  2022-October   1 - 6 2022

ISSN  21624704

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In recent years, there have been many attempts in smart agriculture to increase efficiency and profitability, especially in horticultural agriculture, where profitability is high. One of the measures to achieve this goal is to realize uniform quality by equalizing temperatures in greenhouses which have a huge influence on a process of growth. The most common method for measuring temperatures in greenhouses is the use of temperature sensors. However, to measure continuous temperature distribution by scattering temperature sensors, a large number of temperature sensors must be installed, a method that should be avoided because of its high cost. Therefore, the goal of this paper is to estimate the temperature of substances such as crops and soil in greenhouses, which are secondarily affected by the atmospheric temperature, at a low cost instead of measuring atmospheric temperatures in a costly way. Temperature sensors for substances must be directly attached to the target object to measure its surface temperature, which can lead to the deterioration in quality. In contrast, infrared array sensors can measure the surface temperature of materials from a distance. They have been increasingly used in recent years due to growing demand, and they can be used to measure the surface temperature of a wide range of objects in a greenhouse. However, infrared array sensors also have many operational problems, such as dirty lenses, and the measurement error is larger than that of temperature sensors. Therefore, this paper proposes a machine learning model that predicts continuous temperature distribution in the form of a 16 ×18 pixels heat map from a limited number of temperature sensors. Evaluation results show that our approach is useful in different greenhouse environments, including different airconditioning systems. In addition, the model is computationally inexpensive enough to run in practical fields with limited computational resources; therefore, it can be run on relatively inexpensive embedded terminals. As for the accuracy, the average error of the heat map obtained by the proposed model is as small as 0.28 [°C/pixel].

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Effective Information Selection Method on Spatiotemporal Information Infrastructure with Photogrammetry.

Ayaki Sugawara, Ayu Sonoda, Hiroaki Nishi

IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society(IECON) (IEEE)  2022-October   1 - 6 2022

ISSN  21624704

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In recent years, there has been an increase in the demand for three-dimensional data of geospatial information in various fields, such as automatic driving and disaster prevention. Beyond5G (above 5th generation network), which features wideband, low latency, reliable connection, allows for the easily collection of spatial information of many points and times from camera-equipped vehicles, such as self-driving cars and drones. Spatiotemporal information at any location and at any time can be provided depending on users' requests. However, there are instances where the target point's spatiotemporal information and the request's target time have not been collected. Therefore, appropriate data completion is required to provide this service seamlessly. To create 3D models using photogrammetry, images are required; therefore, multiple videos that captured the streets near our university were prepared and split into frames. Then, those images were stored in our local storage. The information of the recording date, weather, file path to the image, and location (latitude and longitude) were saved to our database per frame. When users request spatiotemporal information, this system commences searching for images that meet the requirements. When the number of matched images are sufficient, the 3D model is created. When it is insufficient, the data are complemented appropriately.

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Anomalous Sound Detection, Extraction, and Localization for Refrigerator Units Using a Microphone Array.

Akihito Nishikawa, Kazuhiro Hattori, Motomasa Tanaka, Hiroaki Muranami, Hiroaki Nishi

IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society(IECON) (IEEE)  2022-October   1 - 6 2022

ISSN  21624704

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Anomaly detection is one of the key applications of data utilization in smart factories, particularly in monitoring factory facilities. Early detection and resolution of anomalies, such as system failures, can lead to cost reduction and quality stabilization. One of the targets of abnormality detection applications in the industry section is a refrigerator unit used in food processing factories and warehouses. Anomalies in the early stages in refrigerator units appear in the operating sounds, which can enable their detection. In this study, we propose a method for detecting abnormal sound, extracting abnormal frequency components, and identifying the direction of the abnormal sound source. To identify the direction of the anomalous sound source, multi-channel sound recorded by a microphone array is used. To the best of our knowledge, no method has yet been proposed for anomaly sound detection using multi-channel acoustic data. In the proposed method, anomaly scores calculated in each channel of the microphone array are aggregated to determine whether the entire data is anomalous or not. Anomalous sounds were extracted from the anomaly data using a deep generative model. The extracted anomalous sounds were used to localize the sound source and the direction of the anomalous source was identified. The proposed method improved the precision of anomaly sound detection while maintaining the recall rate of a conservative comparison method. Using the proposed method, anomalous sounds were extracted from the anomaly data, and their arrival directions were identified.

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An Implementation of a 3D Image Filter for Motion Vector Generation on an FPGA Board.

Yuchen Chen, Kaijie Wei, Hiroaki Nishi, Hideharu Amano

CANDAR (IEEE)     83 - 89 2022

ISSN  2379-1888

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Object detection and tracking technology are essential in many ways with the increasing demand for remote video conferencing and video surveillance. This paper focuses on a method that combines an object detection method based on a CNN (Convolutional Neural Network) with a motion-vector-based object tracking method in specified frames instead of the whole frame stream. Although this method can improve the throughput with a small memory requirement, it requires 3D image filters to reduce the noise in each motion vector. The immense computing power needed for 3D image filters prevents this method from being popularly used. To address this problem, we propose to implement the technique on an FPGA and offload the filters and CNN engine on the FPGA hardwired logic. We implemented slide window-based image filters on a Zynq Ultrascale+ FPGA board and achieved 23.42 times performance improvement for the median filter and 55.46 times for the average filter, respectively. Considering the resource usage, it appeared that we could implement these 3D image filters design with YOLO on the target FPGA.

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Consistency Guaranteed Multi Container Migration for Smart Community Network Services

Abeysiriwardhana W. A. Shanaka P., Morishima Ryo, Miura Tatsuki, Nishi Hiroaki

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan)  141 ( 12 ) 1453 - 1461 2021.12

ISSN  0385-4221

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Smart Community (SC) uses IoT sensors to provide smart grid control, traffic management, and similar IoT services. These services expect to run at the network edge or fog layer to provide low latency network services, encapsulate citizens’ private information, support low-cost IoT terminals from cyber-attacks, and support other cutting-edge fog and edge services. SC edge is a service platform that supports edge/fog services for IoT terminals by using Docker containers. Initially, SC edge/fog computing nodes did not support the function of service migration. However, service migration is necessary to support remote deployment and service distribution in SC networks. The existing container migration techniques focus mainly on resource utilization. However, SC services should handle loss-free data stream processing and order-preservation of network packets to gather IoT sensor data after migration. In addition, SC services require one-to-many migration to support high throughput loads when required. Therefore, this paper focuses on enhancing SC service flexibility by introducing migration for relocatable and network consistency guaranteed containerized services. SC edge proposes multiple container migration techniques that are suitable for network services. The proposed techniques can improve resource consumption, guarantee network traffic consistency, and apply one-to-many migration patterns. Layer leveraging migration (LLM) reduces the overall migration time by 10.8% for an elastic search Docker container than available Docker migration methods. Additionally, consistency guaranteed migration (CGM) is proposed to guarantee network consistency. However, CGM consumes additional resources compared to LLM for IoT data management. Finally, One to N Consistency Guaranteed Migration (O2NCGM) is proposed to support one-to-many migration with data consistency that shows similar performance to CGM.

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Consistency Guaranteed Multi Container Migration for Smart Community Network Services

Abeysiriwardhana W. A. Shanaka P., Morishima Ryo, Miura Tatsuki, Nishi Hiroaki

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan)  141 ( 12 ) 1453 - 1461 2021.12

ISSN  03854221

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<p>Smart Community (SC) uses IoT sensors to provide smart grid control, traffic management, and similar IoT services. These services expect to run at the network edge or fog layer to provide low latency network services, encapsulate citizens’ private information, support low-cost IoT terminals from cyber-attacks, and support other cutting-edge fog and edge services. SC edge is a service platform that supports edge/fog services for IoT terminals by using Docker containers. Initially, SC edge/fog computing nodes did not support the function of service migration. However, service migration is necessary to support remote deployment and service distribution in SC networks. The existing container migration techniques focus mainly on resource utilization. However, SC services should handle loss-free data stream processing and order-preservation of network packets to gather IoT sensor data after migration. In addition, SC services require one-to-many migration to support high throughput loads when required. Therefore, this paper focuses on enhancing SC service flexibility by introducing migration for relocatable and network consistency guaranteed containerized services. SC edge proposes multiple container migration techniques that are suitable for network services. The proposed techniques can improve resource consumption, guarantee network traffic consistency, and apply one-to-many migration patterns. Layer leveraging migration (LLM) reduces the overall migration time by 10.8% for an elastic search Docker container than available Docker migration methods. Additionally, consistency guaranteed migration (CGM) is proposed to guarantee network consistency. However, CGM consumes additional resources compared to LLM for IoT data management. Finally, One to N Consistency Guaranteed Migration (O2NCGM) is proposed to support one-to-many migration with data consistency that shows similar performance to CGM.</p>

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Air-Conditioning Control with Spatial Recognition Using Stereo Infrared Array Sensors

Takayama Y., Saito S., Sakuma Y., Nishi H.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference))  2021-October 2021.10

ISSN  9781665435543

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Depending on the location of the air conditioner and the shape of a room, air-conditioning control may be inefficient resulting in temperature imbalance. When attempting to solve this problem, it is vital to understand the spatial structure of a room (including its size and shape) and the location of air conditioners and then automatically control the airflow and direction according to the structure. However, such a method for recognizing spatial structures has not yet been established. In this paper, we propose a spatial recognition method using stereo infrared array sensors (SIRA sensors) installed in an air conditioner. Our system detects objects in the obtained thermal images and estimates their distances using triangulation. In addition, the room's size and shape are estimated based on the assumption that the room size lies within the detection range. The distances to the front and left/right walls were estimated in one-meter-wide classes. The estimation accuracy was compared using two types of IRA sensors: thermopile array sensors and thermal diode infrared sensors. Regarding the distance estimation of persons from the captured stereo thermal images, the average error rate was 12.5% for both types. The distance to each wall was estimated within a 1 m error range for the thermal diode infrared sensor. Moreover, applications of the proposed spatial recognition to air-conditioning control were demonstrated. Specifically, we propose a method to control the airflow direction and volume by considering the room's geometry. An L-shaped room was modeled and simulated. From the results, the spatial recognition reduced the unevenness in temperature by adjusting the airflow based on the room shape. These results indicate that the proposed method can be practically used for spatial recognition to efficiently improve user comfort by controlling air-conditioning based on the spatial structure and eliminating uneven temperature.

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Time Synchronization of IEEE P1451.0 and P1451.1.6 Standard-based Sensor Networks

Nishi H., Song E.Y., Nakamura Y., Lee K.B., Liu Y., Tsang K.F.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference))  2021-October 2021.10

ISSN  9781665435543

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This paper introduces the time synchronization approaches to the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard-based sensor networks for Internet of Things (IoT) applications. A time synchronization architecture of IEEE P1451.0 standard-based sensor networks is described including two-level time synchronization systems in IEEE P1451.0 and P1451.1.X standards-based wide-area network (WAN) and IEEE P1451.0 and P1451.5.X standards-based local area networks (LANs). However, this paper mainly focuses on the time synchronization approach of IEEE P1451.0 and P1451.1.6 standards-based WANs and provides two implementations of time synchronization of IEEE P1451.0 and P1451.1.6 using wireline and wireless networks with their preliminary results to verify that the time synchronization approach of IEEE P1451.1.6 functions properly. In addition, the time synchronization transducer electronic data sheets (TEDS) of P1451.1.6 is described.

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Recommendation System for Energy Consumption Behavior Change on Residents' Response and Stress

Takayama Y., Sakuma Y., Nishi H.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference))  2021-October 2021.10

ISSN  9781665435543

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Home energy management system (HEMS), enabled by the development of the Internet of Things (IoT), issue behavior change recommendations to encourage residents to reduce their energy consumption. Receiving these suggestions from HEMS makes it easier for them to set specific reduction goals and raise their awareness of energy saving. This feedback will lead to effective power reduction in the household sector. However, each user has unique preferences, and uniformly generated recommendations may not be followed if they do not match the preferences of the specific user. In addition, frequent recommendations that are not aligned with their preferences may stress users and decrease their motivation to reduce energy consumption. This paper presents a practical method of making behavior change recommendations reflecting users' response rates and considering their stress. Targeting the action of opening a window, we illustrate how our system induces behavioral change. To increase the users' response rate and reduce their stress, we adjust the recommendation for each user from two perspectives. First, assuming that users open windows mainly depending on the external temperature, humidity, wind, and weather, we introduce the k-nearest neighbors (k-NN) classification using these parameters as the explanatory variables to predict the possibility that the user accepts the window-opening recommendation. Generating recommendations only when the predicted probability is high enables building a unique recommendation system considering user preferences. Second, if the recommendations are sent frequently, users may become tired of following them; this leads to a situation in which users ignore recommendations or turn off their notifications. To avoid such a situation, we propose adjusting the delivery interval according to the users' response rate. When we schedule the notification cycle, we introduce a forgetting curve, assuming that the users' stress on the recommendation decreases over time. We conducted a simulation using historical weather data. The response rate and thermal sensation of users with different variations were set, and the delivery timing of the recommendation was changed according to these factors. The proposed methods are expected to effectively generate behavioral changes by having users take medium-to long-term initiatives without lowering their motivation.

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Dataflow Management Platform for Smart Communities using an Edge Computing Environment

Shimahara S., Nishi H.

IECON Proceedings (Industrial Electronics Conference) (IECON Proceedings (Industrial Electronics Conference))  2021-October 2021.10

ISSN  9781665435543

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As various data services are provided to realize Society 5.0, the usage of personal data is estimated to increase along with the explosive increase in data traffic. It is important that the protection of privacy keeps pace with increases in the exchange of data containing personal information. Starting from the enforcement of the General Data Protection Regulation (GDPR), stricter privacy protection regulations are expanding to more countries. These restrictions require that personal data should be hidden or anonymized before they are propagated over the network. The secondary usage of data is assumed in smart communities, and systems that can protect privacy are required for secure network infrastructures. In this study, we propose an edge-based computing platform that manages the privacy of users on the network of a smart community. For the platform, we prepared three models: The Basic, Preceding Packet, and Piggyback models. These are considered OpenFlow models, and the network efficiency for each was evaluated.

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Efficient GAN-Based Unsupervised Anomaly Sound Detection for Refrigeration Units

Hatanaka S., Nishi H.

IEEE International Symposium on Industrial Electronics (IEEE International Symposium on Industrial Electronics)  2021-June 2021.06

ISSN  9781728190235

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A smart factory or Industry 4.0 is creating an epoch for manufacturing and its production lines. It reduces the total cost by monitoring and predicting the expected faults of factory lines and products. One of the essential challenges is to develop a technology to detect and predict abnormalities at an early stage without human resources. For this reason, the automation of anomaly detection is now attracting attention. Many statistical and machine-learning methods have been studied for anomaly detection. In this study, we focus on a refrigeration system for large storage, where the failures of the system will cause enormous losses. Moreover, this type of system was independently designed according to the environment, location, and storage items. Under this condition, it is difficult to train discriminative models for anomaly detection using training data that include failure data. In addition, it is indispensable to provide a basis for determining whether the system is abnormal to achieve future treatments. Therefore, deep generative models are used to achieve unsupervised abnormality detection. Because the sensing system's cost for detecting system failures should be reduced, the proposed system uses low-cost microphone arrays to monitor sounds and source locations. The system also provides a rationale by visualizing and mentioning irregular sounds. Furthermore, this study compared various deep generative models in terms of accuracy and showed that the Efficient GAN-based method achieved the highest accuracy.

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Household Nutrition Analysis and Food Recommendation U sing Purchase History

Honda M., Nishi H.

IEEE International Symposium on Industrial Electronics (IEEE International Symposium on Industrial Electronics)  2021-June 2021.06

ISSN  9781728190235

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To prevent non-communicable diseases, it is important to review consumers' dietary habits. Most existing applications for improving eating habits require users to upload photos of their meals and record them manually, but such procedures are time-consuming and laborious. Because the targets of the proposed method are those who are not highly conscious of their health, it is necessary to make the application easy to use. This study applies the history of supermarket purchases to calculate nutrient intake and recommend foods that improve nutritional balance with the least amount of user input. Consequently, the nutrient intake can be estimated with an acceptable error, and foods that are easy for the user to purchase can be recommended. Because the proposed method does not use artificial intelligence technologies to generate recommendations, the reasons for food recommendations are clear and the computational cost is reduced.

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Thermal energy storage performance of tetrabutylammonium acrylate hydrate as phase change materials

Kiyokawa H, Tokutomi H, Ishida S, Nishi H, Ohmura R

Applied Sciences (Switzerland) (Applied Sciences (Switzerland))  11 ( 11 )  2021.06

ISSN  2076-3417

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Kinetic characteristics of thermal energy storage (TES) using tetrabutylammonium acrylate (TBAAc) hydrate were experimentally evaluated for practical use as PCMs. Mechanical agitation or ultrasonic vibration was added to detach the hydrate adhesion on the heat exchanger, which could be a thermal resistance. The effect of the external forces also was evaluated by changing their rotation rate and frequency. When the agitation rate was 600 rpm, the system achieved TES density of 140 MJ/m3 in 2.9 hours. This value is comparable to the ideal performance of ice TES when its solid phase fraction is 45%. UA/V (U: thermal transfer coefficient, A: surface area of the heat ex-change coil, V: volume of the TES medium) is known as an index of the ease of heat transfer in a heat exchanger. UA/V obtained in this study was comparable to that of other common heat exchang-ers, which means the equivalent performance would be available by setting the similar UA/V. In this study, we succeeded in obtaining practical data for heat storage by TBAAc hydrate. The data obtained in this study will be a great help for the practical application of hydrate heat storage in the future.

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Load balancing method using server temperature prediction considering multiple internal heat sources in data centers

Yao X., Omori M., Nishi H.

2021 IEEE International Conference on Mechatronics, ICM 2021 (2021 IEEE International Conference on Mechatronics, ICM 2021)   2021.03

ISSN  9781728144429

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The increasing demand for cloud computing services to more flexibly use computing resources has led to a severe problem of excessive energy consumption in data centers. In recent research, the energy consumption of computing and cooling equipment has attracted much attention. To reduce the energy consumption in data centers, the energy efficiency of the cooling equipment needs to be considered. This study theoretically modeled the relations between the server's internal heat sources and exhaust temperatures. First, we proposed a model that expands the previous study to describe the temperature change delay and to apply it to multiple internal heat sources. Then, we experimentally evaluated the prediction accuracy of the proposed model using a real server and conducted a simulation to confirm the efficiency of this prediction model for load balancing. The simulation results showed that the proposed method reduced the average maximum server temperature by 0.27 °C compared to the conventional method, which theoretically leads to energy savings of 260 kW per day and 95 MW per year in typical data centers.

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Adaptive Control Method of HVAC for Uniformizing Comfort at Japanese Residential Living Rooms Using Deep Reinforcement Learning

Sakuma Yuiko, Nishi Hiroaki

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan)  141 ( 3 ) 373 - 382 2021.03

ISSN  03854221

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<p>There is a great demand for high-quality indoor thermal environments while achieving comfort and saving the energy for heating, ventilation, and air conditioning (HVAC) devices offer a tradeoff. The room shapes, partitioning, and the location of diffusers cause uneven comfort and degrade thermal satisfaction. Therefore, an HVAC control method that provides uniform thermal comfort while saving energy is required. In such cases, providing uniform comfort in a room can be a low-cost and energy-efficient solution. We propose a deep reinforcement learning (DRL)-based HVAC control method to uniformize comfort in a room. The proposed method considers the use of sensors, which are already deployed in existing HVAC devices and is adaptive to rooms with different conditions. The proposed method controls the HVAC device based on the prediction of the room's thermal response using wall heatmaps of the target room. The heatmaps are obtained by using a simple measuring system of an infrared thermopile array sensor. For evaluating the proposed method's adaptivity to rooms with different conditions, a computer flud dynamics (CFD)-based simulation was designed. The coldest period throughout a year was chosen as the evaluation period; uniformizing comfort is the most difficult when the temperature difference between outside and inside a room and the validity of the proposed method for the other periods is implied. The usefulness of the proposed method for the chosen period was shown for the selected period; the proposed method achieved an improvement of 33.9% in thermal comfort performance while on average increasing energy consumption by 6.3%, against that of comparable approaches. Moreover, the comfort performance during learning is confirmed to be similar to those of the existing method.</p>

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Purchasing Behavior Prediction Model for Real Stores Considering Product Cluster and Time-series Patterns

Sakuma Yuiko, Yoshida Masahiro, Nishi Hiroaki

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan)  141 ( 3 ) 471 - 482 2021.03

ISSN  03854221

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<p>Recently, cashless payment services have been rapidly introduced, increasing the demand for recommender systems for real stores. Recommender systems have been well-studied and successful in many Internet services. While factors such as the diversity and coverage of products should be considered, the prediction accuracy of user purchasing behaviors is the most crucial factor in a recommender system. This research proposes a purchasing behavior prediction model for real stores by using purchase history data collected from a supermarket in Saitama City, Japan, as a part of a smart city project. Time-series patterns in purchasing behavior indicate the sales characteristics of supermarkets, where daily products are predominately purchased. For example, some users repeatedly purchase specific products in a certain period or purchase different products from their previous visit. Many previous studies have only considered the compatibility between the features of users and items; however, this work also models the time-series patterns of purchasing behavior. The proposed prediction model adopts ensemble learning, in which weak learners learn the two factors (the compatibility between users and items) and time-series patterns of purchasing behavior separately. Moreover, product names in supermarkets often contain meta-information of the product alongside the item name. For instance, the product name “three tomatoes from Gunma Prefecture” includes information about the quantity and manufacturer as well as the item name “tomato”. However, products that are the same item but have different product names are likely to be purchased together in the context of purchasing activity, thus deteriorating the prediction accuracy. Therefore, a product classification method is proposed to categorize the products for each item. The experimental results show 3.2% and 10.6% improvements in the precision-recall area under the curve (PR-AUC) for the proposed method compared to models that consider only the compatibility between users and items and the time-series patterns of purchasing behavior, respectively. </p>

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特集 スマートシステムと計測・制御技術: 超スマート社会に向けて

Y Sakuma, H Nishi

電気学会論文誌 C (電子・情報・システム部門誌) 141 (3), 373-382  2021

Hardware Implementation of MQTT Broker and Precise Time Synchronization Using IoT Devices

Yamamoto K, Fukuhara A, Nishi H

IEEJ Transactions on Electrical and Electronic Engineering (IEEJ Transactions on Electrical and Electronic Engineering)  17 ( 2 ) 209 - 217 2021

ISSN  19314973

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In recent years, while internet of things (IoT) devices and cloud computing environments have been penetrated, the increase in communication latency and concentration of traffic caused by data centers has deteriorated the quality of some network services. Message Queuing Telemetry Transport (MQTT) is a well-known protocol for exchanging messages between dedicated applications. MQTT employs a publish–subscribe model in which a broker mediates communication between IoT devices and applications. To improve service quality, the processing latency of the broker should be reduced. Moreover, the broker's throughput must be increased. IoT devices, in particular, must maintain a highly accurate time to provide some IoT services. This study proposed hardware implementation of MQTT broker and evaluated the accuracy of an MQTT-based time synchronization method. The proposed MQTT architecture's hardware resource utilization is also provided. We confirmed that our implementation achieved high throughput, low latency, and low jitter MQTT broker, which satisfies the smart city services for automated drive and power control, which must be less than a few milliseconds. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Digital Watermarking for Anonymized Data With Low Information Loss.

Yuichi Nakamura, Hiroaki Nishi

IEEE Access (IEEE Access)  9   130570 - 130585 2021

ISSN  2169-3536

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At present, massive amounts of data are utilized for artificial intelligence technologies such as machine learning and deep learning. However, these data must be utilized carefully while preserving data privacy. Data anonymization is a technique enabling both data mining and privacy protection, preventing the identification of individuals by generalizing the data to include multiple records with the same values. In this study, we consider a data-publishing infrastructure for personal data sharing. The infrastructure anonymizes data prior to publishing it to users for privacy protection; however, the problem of unauthorized republishing by malicious users must be considered. To address this issue, we studied digital watermarking methods that correlate data users with anonymized data. Our previous method embedded information indicating the original user to detect illegally republished data. However, this method did not focus on information loss. This study proposes another digital watermarking method for anonymized data that achieves low information loss. The proposed method replaces values in tuples to embed information. To reduce the information loss caused by the embedding, the proposed method selects replacement values from the candidates whose meanings are similar to the original. We propose the use of vector-conversion tables to select replacement values. The proposed method also extends the maximum length of the embedded bit string by embedding multiple bits into a single tuple. Moreover, we measured the tolerance to distortion attacks to evaluate the efficacy of the proposed method. The proposed method is non-blind, i.e., data prior to digital watermarking is required to perform extraction.

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Trajectory Anonymization through Laplace Noise Addition in Latent Space.

Yuiko Sakuma, Thai P. Tran, Tomomu Iwai, Akihito Nishikawa, Hiroaki Nishi

Ninth International Symposium on Computing and Networking(CANDAR) (IEEE)     65 - 73 2021

ISSN  2379-1888

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In recent years, the volume of captured location-based movement data has drastically increased with the prevalence of smartphones. Mobility data are commonly used for smart assistant and personalized advertising applications. However, such data contain considerable sensitive information; thus, they must be anonymized before they can be published or analyzed. In this study, we investigate the problem of anonymization for trajectory publication. Anonymizing trajectories is challenging because they have high dimensionality in both the spatial and temporal domains. Traditional anonymization methods cannot handle high dimensionality without significantly sacrificing data utility. The proposed method addresses this limitation by training a Seq2Seq autoencoder model to reconstruct trajectories from the spatiotemporal input, followed by distributing the Laplace noise to the principal components of the Seq2Seq encoder's hidden-layer output under differential privacy. By distributing the privacy budget in the latent space, the proposed method can output trajectories that satisfy differential privacy while maintaining embedded information. Experimental results from the application of the proposed method to real-life movement trajectory data from Saitama, Japan, demonstrate a reduction in data loss by up to 75.7 % while maintaining significant data utility.

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