• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4766-4786
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• 2016

Looking for missing companions who are out of touch in public places might suffer a long and painful process. With the help of mobile crowdsourcing, the missing person's location may be reported in a short time. In this paper, we propose MissingFound, an assistant system that applies mobile crowdsourcing for finding missing companions. Discovering valuable users who have chances to see the missing person is the most important task of MissingFound but also a big challenge with the requirements of saving battery and protecting users' location privacy. A customized metric is designed to measure the probability of seeing, according to users' movement traces represented by WiFi RSSI fingerprints. Since WiFi RSSI fingerprints provide no knowledge of users' physical locations, the computation of probability is too complex for practical use. By parallelizing the original sequential algorithms under MapReduce framework, the selecting process can be accomplished within a few minutes for 10 thousand users with records of several days. Experimental evaluation with 23 volunteers shows that MissingFound can select out the potential witnesses in reality and achieves a high accuracy (76.75% on average). We believe that MissingFound can help not only find missing companions, but other public services (e.g., controlling communicable diseases).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권8호
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• pp.2827-2848
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• 2021

Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group under considerations of privacy and efficiency.

• 한국콘텐츠학회논문지
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• 제22권4호
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• pp.83-93
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• 2022

운전자가 모바일기기를 사용하여 직접 교통 정보를 제공하는 크라우드 소싱을 활용하여 교통 문제를 해결하려는 연구들이 진행 중이다. 크라우드 소싱을 통해 수집된 데이터를 교통 이벤트 검출에 사용한다면 관련된 데이터를 수집하는 작업이 줄어들어 시간 비용이 낮아지고 정확도는 높아지는 장점이 있다. 본 논문에서는 크라우드 소싱을 활용하여 교통과 관련된 데이터를 수집하고, 이를 통해 교통에 영향을 미치는 이벤트를 검출하는 기법을 제안한다. 제안하는 기법은 대용량 데이터 처리를 위해 기계 학습 알고리즘을 사용하여 수집된 데이터의 이벤트 유형을 판별한다. 또한, 이벤트가 발생된 위치를 추출하기 위하여 수집된 데이터에서 위치를 나타내는 키워드를 추출하고 키워드의 행정구역을 반환한다. 이를 통해 기존 제공되는 위치 정보에서 광범위하게 정의된 위치나 잘못된 위치 정보를 해결할 수 있다. 제안하는 기법의 타당성을 입증하기 위해 다양한 성능 평가를 수행한다.

• 농촌계획
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• 제21권3호
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• pp.33-45
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• 2015

The oversea export of agricultural-product about item and quantity has not increased recently; especially the fresh-product has a tough issue because of period of production, price large fluctuations, customs clearance, quarantine, and uncertainty about actual locality, we need the information based construction to exchange information quickly about whole range of export and to focus capacity of participation subject for increasing the export. In this study we design the agricultural-product transaction information system based on crowdsourcing to transact the agricultural-product and the information of influencing benefit directly, and the information offering about export-procedure from participation of customs clearance, finance, distribution, buyer, and producer's guild, etc. We expect the producer's guild about agriculture that has not participate the trade to be able to export the agricultural-product and the stabilization of price to transact the product of collapsed or boomed through the agricultural-product information system based on crowdsourcing.

• 한국컴퓨터정보학회논문지
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• 제24권3호
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• pp.95-104
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• 2019

Crowdsourcing is growing its interests in designing where various designers work independently to a given design task. Recent researchers discovered that collaboration by sharing designs among designers helps to produce high-quality designs. However, design task may still be hard even with that collaboration in case the requirements are not well-defined. Most customers sometimes do not know what they really want and do not know how to clearly define the requirements. Consequently, the lack of requirements creates issues on designers, such as spending much time and effort on collecting requirements alone or from the customers. The designers even end up missing important necessities to complete their tasks. To address this issue, we proposed a collaborative requirements elicitation method that supports designers who are working on the same task. We developed CREFD (Collaborative Requirements Elicitation For Designers and Developers) tool to enable designers collaboratively provide requirements, identify dependencies, add annotations and votes to the provided requirements. We performed the hypothetical and empirical evaluations to test and compare the proposed method with one of the existing elicitation methods, the results show that the proposed method helps in collecting accepted and well-organized requirements better than individual requirements elicitation.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.199-208
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• 2022

The world health organization classified the emerging coronavirus (known as Covid-19) as a pandemic after confirming the extent of spread and scale. As a matter of fact, outbreaks of similar scale or even worse have been witnessed throughout history. Thus, the development of prevention strategies exists to protect against such calamaties. One of the widely proven measures that controls the spread of any contagious diseases is social distancing. As a result, this paper will demonstrate the concept of an application "Be Aware" on enabling the implementation of this preventive measure. In particular "Be aware" evaluates the extent of congestion in public places using current time data. The proposed project will use Global Positioning System (GPS), and Application Programming Interface (API), to ensure information accuracy, and the API use Crowdsourcing to collect Real-Time Data (RTD) from the selected places. One line

• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.827-834
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• 2017

Android's inter-application access enriches its application ecosystem. However, it exposes security vulnerabilities where end-user data can be exploited by attackers. While existing techniques have focused on minimizing the risks of inter-application access, they either suffer from inaccurate risk detection or are primarily available to expert users. This paper introduces a novel technique that automatically analyzes potential risks between a set of applications, aids end-users to effectively assess the identified risks by crowdsourcing assessments, and generates an access control policy which prevents unsafe inter-application access at runtime. Our evaluation demonstrated that our technique identifies potential risks between real-world applications with perfect accuracy, supports a scalable analysis on a large number of applications, and successfully aids end-users' risk assessments.

• Architectural research
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• 제20권4호
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• pp.109-120
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• 2018

In the context of ever-faster urbanization, cities are becoming increasingly complex, and data collection to understand such complex relationships is becoming a very important factor. This paper focuses on the lighter weight of the method of collecting urban data, and studied how to use such complementary data collection using crowdsourcing. Especially, the method of converting mobile acceleration sensor information to urban trip information by combining with locational information was experimented. Using the parameters for transportation type classification obtained from the research, information was obtained and verified in Singapore and Zurich. The result of this study is thought to be a good example of how to combine raw data into meaningful behavior information.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.61-63
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• 2019

본 논문에서는 웹툰 및 웹툰 플랫폼이 효율적으로 국외 시장에 진출하거나 또는 작품 원문의 언어를 사용하지 않는 고객에게 효과적으로 서비스 할 수 있도록 크라우드 소싱을 이용한 번역 및 투고 시스템과 이를 적용한 서비스 매체를 제안한다. 제안하는 시스템은 콘텐츠 번역 자원을 크라우드 소싱을 통해 효율적으로 확보한다. 이를 통해 콘텐츠를 세계화하는데 투입되는 비용을 최소화한다. 또한 디지털 콘텐츠가 디지털 매체를 통해 제공되므로 소프트웨어를 응용 할 수 있는 환경 특성을 이용하여 효과적으로 콘텐츠 내용을 관리하고 검증 할 수 있다. 본 논문에서 제안하는 크라우드 소싱을 활용한 번역 및 투고 시스템을 적용한 디지털 콘텐츠 및 서비스 매체는 기존 디지털 콘텐츠 및 서비스 매체에 비해 콘텐츠 세계화에 필요로 하는 비용효율 면에서 우수함을 보인다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.864-867
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• 2021

뉴스, SNS 등의 인터넷 댓글은 익명으로 의견을 자유롭게 개진할 수 있는 반면 댓글의 익명성을 악용하여 비방이나 험담을 하는 악성 댓글이 여러 분야에서 사회적 문제가 되고 있다. 해당 문제를 해결하기 위해 AI를 활용한 댓글 분류 알고리즘을 개발하려는 많은 노력들이 이루어지고 있지만, 댓글 분류 모델에 사용되는 AI는 오버피팅의 문제로 인해 댓글 분류에 대한 정확도가 떨어지는 문제점을 가지고 있다. 이에 본 연구에서는 크라우드소싱을 활용하여 오버피팅으로 인한 악성 댓글 분류 및 판단 정확도 저하 문제를 개선한 크라우드소싱 기반 딥러닝 분류 알고리즘(Deep Learning Classification Algorithm Based on Crowdsourcing: DCAC)과 해당 알고리즘을 사용한 시스템을 제안한다. 또한, 실험을 통해 오버피팅으로 낮아진 판단 정확도를 증가시키는 데 제안된 방법이 도움이 되는 것을 확인하였다.