Abstract
The explosive growth and widespread accessibility of digital data have led to a surge of research activity in the machine learning field. Typically a massive data collection is required to increase the quality of machine learning result. Often, these data contained highly sensitive information such as medical history, or financial records. Hence, privacy concerns have overshadowed by other factors in today's machine learning systems. A fundamental problem in privacy-preserving machine learning (PPML) is how to make the right tradeoff between privacy and utility. On the one hand, the PPML solution must not allow the original data records (e.g., training data) to be adequately recovered (i.e., privacy loss). On the other, it must allow the system to learn the model that is closely approximates to the model that is trained using the original data (i.e., utility gain). In this chapter, we will discuss several emerging technologies that can be used to protect privacy in machine learning systems. In addition, we also provide a state-of-the-art of the adoption of privacy preserving schemes in decision tree algorithms.
| Original language | English |
|---|---|
| Title of host publication | Security and Authentication |
| Subtitle of host publication | Perspectives, Management and Challenges |
| Publisher | Nova Science Publishers, Inc. |
| Pages | 13-39 |
| Number of pages | 27 |
| ISBN (Electronic) | 9781536129434 |
| ISBN (Print) | 9781536129427 |
| Publication status | Published - Jan 1 2017 |
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Keywords
- C4.5
- Classification
- ID3
- Machine learning
- Privacy-preserving
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Privacy protection in machine learning : The state-of-the-art for a private decision tree. / Chew, Yee Jian; Wong, Kok Seng; Ooi, Shih Yin.
Security and Authentication: Perspectives, Management and Challenges. Nova Science Publishers, Inc., 2017. p. 13-39.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Privacy protection in machine learning
T2 - The state-of-the-art for a private decision tree
AU - Chew, Yee Jian
AU - Wong, Kok Seng
AU - Ooi, Shih Yin
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The explosive growth and widespread accessibility of digital data have led to a surge of research activity in the machine learning field. Typically a massive data collection is required to increase the quality of machine learning result. Often, these data contained highly sensitive information such as medical history, or financial records. Hence, privacy concerns have overshadowed by other factors in today's machine learning systems. A fundamental problem in privacy-preserving machine learning (PPML) is how to make the right tradeoff between privacy and utility. On the one hand, the PPML solution must not allow the original data records (e.g., training data) to be adequately recovered (i.e., privacy loss). On the other, it must allow the system to learn the model that is closely approximates to the model that is trained using the original data (i.e., utility gain). In this chapter, we will discuss several emerging technologies that can be used to protect privacy in machine learning systems. In addition, we also provide a state-of-the-art of the adoption of privacy preserving schemes in decision tree algorithms.
AB - The explosive growth and widespread accessibility of digital data have led to a surge of research activity in the machine learning field. Typically a massive data collection is required to increase the quality of machine learning result. Often, these data contained highly sensitive information such as medical history, or financial records. Hence, privacy concerns have overshadowed by other factors in today's machine learning systems. A fundamental problem in privacy-preserving machine learning (PPML) is how to make the right tradeoff between privacy and utility. On the one hand, the PPML solution must not allow the original data records (e.g., training data) to be adequately recovered (i.e., privacy loss). On the other, it must allow the system to learn the model that is closely approximates to the model that is trained using the original data (i.e., utility gain). In this chapter, we will discuss several emerging technologies that can be used to protect privacy in machine learning systems. In addition, we also provide a state-of-the-art of the adoption of privacy preserving schemes in decision tree algorithms.
KW - C4.5
KW - Classification
KW - ID3
KW - Machine learning
KW - Privacy-preserving
UR - http://www.scopus.com/inward/record.url?scp=85044637604&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044637604&partnerID=8YFLogxK
M3 - Chapter
AN - SCOPUS:85044637604
SN - 9781536129427
SP - 13
EP - 39
BT - Security and Authentication
PB - Nova Science Publishers, Inc.
ER -