디지털융복합연구 (Journal of Digital Convergence)

  • 제17권7호
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  • Pages.285-292
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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신경망 학습에서 프라이버시 이슈 및 대응방법 분석

Analysis of privacy issues and countermeasures in neural network learning

  • Hong, Eun-Ju (Dept. of Convergence Science, Kongju National University) ;
  • Lee, Su-Jin (Dept. of Mathematics, Kongju National University) ;
  • Hong, Do-won (Dept. of Applied Mathematics, Kongju National University) ;
  • Seo, Chang-Ho (Dept. of Applied Mathematics, Kongju National University)
  • 투고 : 2019.04.11
  • 심사 : 2019.07.20
  • 발행 : 2019.07.28
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⟨ 이전 논문 다음 논문 ⟩

초록

PC, SNS, IoT의 대중화로 수많은 데이터가 생성되고 그 양은 기하급수적으로 증가하고 있다. 거대한 양의 데이터를 활용하는 방법으로 인공신경망 학습은 최근 많은 분야에서 주목받는 주제이다. 인공신경망 학습은 음성인식, 이미지 인식에서 엄청난 잠재력을 보였으며 더 나아가 의료진단, 인공지능 게임 및 얼굴인식 등 다양하고 복잡한 곳에 광범위하게 적용된다. 인공신경망의 결과는 실제 인간을 능가할 정도로 정확성을 보이고 있다. 이러한 많은 이점에도 불구하고 인공신경망 학습에는 여전히 프라이버시 문제가 존재한다. 인공신경망 학습을 위한 학습 데이터에는 개인의 민감한 정보를 포함한 다양한 정보가 포함되어 악의적인 공격자로 인해 프라이버시가 노출될 수 있다. 공격자가 학습하는 도중 개입하여 학습이 저하되거나 학습이 완료된 모델을 공격할 때 발생하는 프라이버시 위험이 있다. 본 논문에서는 최근 제안된 신경망 모델의 공격 기법과 그에 따른 프라이버시 보호 방법을 분석한다.

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.

키워드

DJTJBT_2019_v17n7_285_f0001.png 이미지

Fig. 1. Neural Network

DJTJBT_2019_v17n7_285_f0002.png 이미지

Fig. 2. Differential Privacy[7]

DJTJBT_2019_v17n7_285_f0003.png 이미지

Fig. 3. Centralized and Distributed training model

DJTJBT_2019_v17n7_285_f0004.png 이미지

Fig. 4. GAN attack

DJTJBT_2019_v17n7_285_f0005.png 이미지

Fig. 5. Model Extraction Attack

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