Context-Aware Generative Adversarial Privacy

Huang, Chong; Kairouz, Peter; Chen, Xiao; Sankar, Lalitha; Rajagopal, Ram

doi:10.3390/e19120656

Cited by 132 publications

(123 citation statements)

References 82 publications

(123 reference statements)

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“…Differential privacy has been widely used to quantifying privacy issues related to databases [2]. More recently, generative adversarial privacy has been proposed [3]. In both cases, if users are present in multiple databases, knowledge of alignment is required to fully apply these frameworks.…”

Section: The Database Deanonymization Problemmentioning

confidence: 99%

Fundamental Limits of Database Alignment

Cullina

Mittal

Kiyavash

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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We consider the problem of aligning a pair of databases with correlated entries. We introduce a new measure of correlation in a joint distribution that we call cycle mutual information. This measure has operational significance: it determines whether exact recovery of the correspondence between database entries is possible for any algorithm. Additionally, there is an efficient algorithm for database alignment that achieves this information theoretic threshold.

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Section: The Database Deanonymization Problemmentioning

confidence: 99%

Fundamental Limits of Database Alignment

Cullina

Mittal

Kiyavash

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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“…Then, they approximately solve the optimization problem using the cluster centroids. Huang et al [58] proposed to use generative adversarial networks to approximately solve the game-theoretic optimization problems. However, these approximate solutions do not have formal guarantees on utility loss of the public data.…”

Section: Defensesmentioning

confidence: 99%

Defending Against Machine Learning Based Inference Attacks via Adversarial Examples: Opportunities and Challenges

Jia

Gong

2020

Adaptive Autonomous Secure Cyber Systems

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As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various domains. In such an ML-equipped inference attack, an attacker has access to some data (called public data) of an individual, a software, or a system; and the attacker uses an ML classifier to automatically infer their private data. Inference attacks pose severe privacy and security threats to individuals and systems. Inference attacks are successful because private data are statistically correlated with public data, and ML classifiers can capture such statistical correlations. In this chapter, we discuss the opportunities and challenges of defending against ML-equipped inference attacks via adversarial examples. Our key observation is that attackers rely on ML classifiers in inference attacks. The adversarial machine learning community has demonstrated that ML classifiers have various vulnerabilities. Therefore, we can turn the vulnerabilities of ML into defenses against inference attacks. For example, ML classifiers are vulnerable to adversarial examples, which add carefully crafted noise to normal examples such that an ML classifier makes predictions for the examples as we desire. To defend against inference attacks, we can add carefully crafted noise into the public data to turn them into adversarial examples, such that attackers' classifiers make incorrect predictions for the private data. However, existing methods to construct adversarial examples are insufficient because they did not consider the unique challenges and requirements for the crafted noise at defending against inference attacks. In this chapter, we take defending against inference attacks in online social networks as an example to illustrate the opportunities and challenges.

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“…removing text from images [7]. An optimal privacy mechanism can be formulated as a game between two players, a privatizer and an adversary, with an iterative minimax algorithm [11]. Moreover, the service provider can share a feature extractor based on an initial training set that is then re-trained by the user on their data and then sent back to the service provider [24,30].…”

Section: Related Workmentioning

confidence: 99%

Mobile sensor data anonymization

Malekzadeh

Clegg

Cavallaro

et al. 2019

Proceedings of the International Conference on Internet of Things Design and Implementation

161

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Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.

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Context-Aware Generative Adversarial Privacy

Cited by 132 publications

References 82 publications

Fundamental Limits of Database Alignment

Fundamental Limits of Database Alignment

Defending Against Machine Learning Based Inference Attacks via Adversarial Examples: Opportunities and Challenges

Mobile sensor data anonymization

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