Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy

Chen, Hung-Li; Chen, Jiayang; Tsou, Yao-Tung; Yu, Chia-Fu; Tai, Bo-Chen; Li, Szu-Chuang; Huang, Yennun; Lin, Chia-Ming

doi:10.1109/prdc.2017.59

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Some previous researches have been done for privacy metrics. Chen et al proposed an evaluation method on the risk of data disclosure under differential privacy and it is based on noise estimation [86]. Zhao et al discussed the privacy metrics on vehicular communication technologies and authors compared 41 privacy metrics in terms of four novel criteria [87].…”

Section: Metrics For Differential Privacy In Iiotmentioning

confidence: 99%

“…The solution of the contradiction between privacy protection and data utility is a research hotspot in the field of privacy protection, which will be discussed in Section V. Aiming at the problem of privacy and utility balance in the off-line data publishing scenario of differential privacy, the optimal differential privacy mechanism to balance privacy and data utility is studied by using rate distortion theory [26]. Based on the communication theory, such method abstracts the noise channel model of differential privacy, measures the privacy and utility of data publishing by mutual information and distortion function, and constructs the optimization model based on rate distortion theory [86].…”

Section: Utility Optimization With Rate Distortionmentioning

confidence: 99%

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Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Jiang,

Li,

Yue

et al. 2021

Preprint

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The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.

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Section: Metrics For Differential Privacy In Iiotmentioning

confidence: 99%

Section: Utility Optimization With Rate Distortionmentioning

confidence: 99%

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Jiang,

Li,

Yue

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…These raw logs contain detailed attack activities. For each log message, we wrote scripts to remove specific (e.g., personal information [24], [25] or IP addresses) and non-deterministic information (e.g., time) and keep only the event in E in the form of a symbolic name. For example, download _ sensitive denotes a download of a file with a sensitive extension, e.g., .c, from an arbitrary node at any time.…”

Section: B Severity Repetitiveness and Commonalitymentioning

confidence: 99%

On Preempting Advanced Persistent Threats Using Probabilistic Graphical Models

Cao¹

2019

Preprint

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This paper presents PULSAR, a framework for preempting Advanced Persistent Threats (APTs). PULSAR employs a probabilistic graphical model (specifically a Factor Graph) to infer the time evolution of an attack based on observed security events at runtime. The framework (i) learns the statistical significance of patterns of events from past attacks; (ii) composes these patterns into FGs to capture the progression of the attack; and (iii) decides on preemptive actions. The accuracy of our approach and its performance are evaluated in three experiments at SystemX: (i) a study with a dataset containing 120 successful APTs over the past 10 years (PULSAR accurately identifies 91.7%); (ii) replaying of a set of ten unseen APTs (PULSAR stops 8 out of 10 replayed attacks before system integrity violation, and all ten before data exfiltration); and (iii) a production deployment of the framework (during a month-long deployment, PULSAR took an average of one second to make a decision).

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“…Holohan et al [2017] design the (k, )-anonymity algorithm for quasi-identifiers and evaluate its privacy protection against linking records in the perturbed data to the original records using the nearest neighbor technique. Chen et al [2017] calculate the disclosure risk associated with sanitized data with numerical and binary attributes via the Laplace mechanism of -DP.…”

Section: Introductionmentioning

confidence: 99%

Disclosure Risk from Homogeneity Attack in Differentially Private Frequency Distribution

Fang

Zhao²,

Zhang³

2021

Preprint

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Homogeneity attack allows adversaries to obtain the exact values on the sensitive attributes for his targets without having to re-identify them from released data. Differential privacy (DP) is a mathematical concept that provides robust privacy guarantee against a wide range of privacy attacks. We propose a measure for disclosure risk from homogeneity attack; and derive closed-form relationships between the privacy loss parameters from DP and the disclosure risk from homogeneity attack when released data are multi-dimensional frequency distributions. The availability of the close-form relationships not only saves time and computational resources spent on calculating the relationships numerically, but also assists understanding of DP and privacy loss parameters by putting the abstract concepts in the context of a concrete privacy attack, and offers a different perspective when it comes to choosing privacy loss parameters and implementing differentially private mechanisms for data sanitization and release in practice. We apply the closed-form mathematical relationships in real-life data sets and demonstrate their consistency with the empirical assessment of the disclosure risk due to homogeneity attack on sanitized data.

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Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy

Cited by 7 publications

References 13 publications

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

On Preempting Advanced Persistent Threats Using Probabilistic Graphical Models

Disclosure Risk from Homogeneity Attack in Differentially Private Frequency Distribution

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