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Private Data Inference Attacks against Cloud : Model, Technologies, and Research Directions

Machine learning models are established with a variety of data collected from individual users who are concerned about their privacy. Various cloud service providers (e.g., Amazon, Google, Alibaba) commercialize their models by selling them or providing limited access via APIs. However, existing studies have shown that such cloud-based models are susceptible to training data inference attacks (i.e., membership inference attacks, attribute inference attacks, and model inversion attacks) since the trained models remember information about the training data. In this article, we perform an exhaustive investigation on the current training data inference attacks against cloud-based models. According to the attack scenario, we divide the existing inference attacks into two categories: centralized and collaborative learning scenarios. We first give a comprehensive review of attacks in each category and then give a qualitative comparison of the existing attacks. Further, we quantitatively compare the performance of different attack strategies by experiments. Last but not least, we pinpoint some open issues and potential future directions that need to be investigated in this area.

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Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.60 No.9 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 46-52

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
COLLABORATION
CLOUD COMPUTING
DATA MODELS
PREDICTIVE MODELS
COMMERCIALIZATION
INFERENCE ALGORITHMS
COLLABORATIVE WORK
TRAINING DATA
SERVERS

Info Detail Spesifik

DOI: 10.1109/MCOM.004.2100867

Pernyataan Tanggungjawab

Xueluan Gong, Yanjiao Chen, Qian Wang, Meng Wang, Shuyang Li

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