Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Sei, Yuichi; Ohsuga, Akihiko

doi:10.1109/access.2022.3143813

Cited by 7 publications

(16 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We changed the value from 0.1 to 10.0. This range of covers the main values used in existing studies in scenarios in which individuals collect data [15], [25], [29], [35]. Fig.…”

Section: B Results For Differentially Private Datasetsmentioning

confidence: 99%

Re-Identification in Differentially Private Incomplete Datasets

Sei

Okumura

Ohsuga

2022

IEEE Open J. Comput. Soc.

Self Cite

View full text Add to dashboard Cite

Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and sociological data. To make data available to many researchers who can conduct statistical analyses and machine learning, personally identifiable information must be excluded to protect individual privacy. It is essential to remove explicit identifiers, sample population data, and apply differential privacy, the de facto standard privacy metric. Despite the general belief that the risk of re-identification is insignificant when these techniques are applied, this study shows that even after applying these techniques, the risk of being re-identified is highly significant for some data. This study proposes in detail an algorithm for estimating the number of people in a population who have certain attribute values based on incomplete, differentially private databases. If the estimated number is one, the probability that only one person with that attribute value is present in the population is high, which means that there is a high probability of re-identification. Therefore, this study concludes that the re-identification risk must be evaluated even after applying state-of-the-art techniques to protect privacy.INDEX TERMS Differential privacy, ethical and privacy framework, re-identification.

show abstract

“…We changed the value from 0.1 to 10.0. This range of covers the main values used in existing studies in scenarios in which individuals collect data [15], [25], [29], [35]. Fig.…”

Section: B Results For Differentially Private Datasetsmentioning

confidence: 99%

Re-Identification in Differentially Private Incomplete Datasets

Sei

Okumura

Ohsuga

2022

IEEE Open J. Comput. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sei et al [23] proposed the concept of true-valuebased differential privacy (TDP). This is a privacy guarantee standard that considers the fact that the values measured using IoT devices contain errors.…”

Section: True-value-based Differential Privacymentioning

confidence: 99%

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Isawa,

Sei,

Tahara

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

In recent years, systems that collect location information and publish statistics, such as those that publish congestion information, have been extensively employed. Because it is possible to infer an individual’s identity even if the information is not directly disclosed, it is essential to disclose data with privacy protection. Therefore, privacy protection methods based on differential privacy are attracting attention. Geo-indistinguishability is the most famous extension theorem of differential privacy for location information. Geo-indistinguishability can be achieved by adding noise to a target value that must be protected. However, noise addition reduces the usefulness of the data. Thus, it is desirable to add minimal noise to your privacy budget. Therefore, we focus on the fact that the values obtained using measurement devices contain errors. We introduced a novel concept of differential privacy tailored for location information, termed true-value-based geo-indistinguishability (T-Geo-I), which accounts for equipment noise. We also proposed a location information privacy protection method that considers T-Geo-I and reduces the amount of added noise. The object of privacy protection should be the “true value” not the “measured value” that includes measurement errors. According to the experimental results, in the case wherein the measurement error is the normal distribution, our method reduced the noise average and mean square error (MSE) by up to 41% and 63%, respectively, compared with conventional methods while maintaining a prespecified level of privacy in 108 samples of numerical data. In the case wherein the measurement error is the lognormal distribution, the proposed method based on T-Geo-I succeeded in reducing the noise average and MSE by up to 60% and 67%, respectively, compared with methods based on Geo-I, while maintaining a prespecified level of privacy. These findings indicate that the proposed method can improve the usefulness of data while maintaining a prespecified degree of privacy protection.

show abstract

“…True-value-based differential privacy (TDP) is a novel privacy model proposed in [69] that applies traditional differential privacy to the "true value" unknown by the data owner or anonymizer but not to the "measured value" containing errors. Due to sensing errors, available data may differ from true data in many cases, particularly in the Internet of Things (IoT).…”

Section: Recent Work Of Data Mining In Iotmentioning

confidence: 99%

Data Mining in Internet of Things Systems: A Literature Review

Sarhan

2023

Journal of Engineering Research

View full text Add to dashboard Cite

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included.

show abstract

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Cited by 7 publications

References 70 publications

Re-Identification in Differentially Private Incomplete Datasets

Re-Identification in Differentially Private Incomplete Datasets

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Data Mining in Internet of Things Systems: A Literature Review

Contact Info

Product

Resources

About