Utilizing Noise Addition for Data Privacy, an Overview

Mivule, Kato

doi:10.48550/arxiv.1309.3958

Cited by 16 publications

(15 citation statements)

References 26 publications

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“…The authors in [29] introduced a model for local obfuscation of probability distribution by probability perturbation, which perturbed each single "point" datum by adding controlled probabilistic noise before sending it out to a data collector. The authors in [30] studied noise addition as a data privacy providing technique.…”

Section: A Additivementioning

confidence: 99%

“…Obfuscation technique Smart grid Comments [16] Additive Yes Used Laplace distribution [17] Additive Yes Used Gaussian distribution [18] Additive Yes Used Gaussian distribution [23] Additive Yes Used Laplace distribution [24] Additive Yes Used uniform distribution [25] Miscellaneous Yes Used GMM [27] Additive Yes Used correlated Gaussian noise [28] Additive Yes Used Laplace distribution [29] Additive No Used controlled probabilistic noise [30] Additive No Studied noise addition [31] Multiplicative No Used truncated triangular distribution [32] Multiplicative No Used orthogonal matrices [33] Multiplicative No Showed that if original and perturbed data are highly correlated, a malicious entity can recover the original data [34] Multiplicative No Stated that multiplicative noise has the advantage of perturbation size directly proportional to the original data value [35] Multiplicative No Evaluated linear and non-linear schemes [36] Additive + Yes Showed that data hiding and additive noise could hinder consumer attributes idetification [37] Additive + Yes Used uniform noise followed by homomorphic encryption [38] Additive + No Either added random noise or augmented the data with fake noisy samples [39] Additive + No Studied obfuscation and anonymization, considered privacy but not utility [40] Miscellaneous Yes Used a sparse dictionary [41] Miscellaneous No Used GANs for privacy-reservation of mobile datasets [42] Miscellaneous Yes Studied linear and data shuffling masking methods that required knowledge of the whole data set [43] Miscellaneous Yes Replaced high-risk data with alternative data data by using linear regression for a large number of consumers.…”

Section: Ref Nomentioning

confidence: 99%

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Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

et al. 2022

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In this paper, we compare and analyze light-weight approaches for instantaneous smart meter (SM) data obfuscation from a group of consumers. In the literature, the common approach is to use additive Gaussian noise based SM data obfuscation. In order to investigate the effects of different approaches, we consider Gaussian, Rayleigh, generalized Gaussian and chi-square distributions to achieve either additive or multiplicative data obfuscation. For each type of obfuscation approach, we calculate the required parameters to achieve obfuscation such that 50% of the obfuscated data fall outside an interval equalling twice the mean of the instantaneous SM measurements. We also calculate the minimum number of SMs required to estimate the mean of the actual SM measurements, such that the estimate varies within only 0.5% of the actual mean with a 99.5% probability. Simulation results are used to verify the calculations, and it is shown that multiplicative Rayleigh and generalized Gaussian noise require the least number of SMs, which is 90% less than the traditional approach of additive Gaussian noise-based SM data obfuscation.

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Section: A Additivementioning

confidence: 99%

Section: Ref Nomentioning

confidence: 99%

Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

et al. 2022

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“…Indeed, complex noise transformations severely alter the semantics of data resulting in a significant utility loss, as described in [11], [14]. On the other hand, simple noise distortion techniques can be reverted to obtain the original microdata set, as demonstrated by [11], [15], [16].…”

Section: Anonymization Techniquesmentioning

confidence: 99%

You can't always get what you want: towards user-controlled privacy on Android

Caputo,

Pagano,

Bottino

et al. 2021

Preprint

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Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user -i.e., the actual owner of the data -is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries contained in the 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. Then, we propose an empowered anonymization methodology, based on MobHide [1], that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of such anonymization methodology thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.

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“…Perturbation distorts the data by adding noise, aggregating values, swapping values, or generating synthesized data based on statistical properties of the original [25]. Since these records do not correspond to real-world owners, the attacker cannot perform the same sensitive linkage attacks found with generalization.…”

Section: Noisementioning

confidence: 99%

“…This transformation of sensitive values works by adding or multiplying a stochastic or randomized value. The stochastic value is chosen from the "normal" distribution with zero mean and a diminutive standard deviation [16], [25]. Additive noise, also known as white noise, is adding a stochastic value to the original sensitive value which then replaces the original for publication, and this is known as the the transformed data point, which preservers the mean and co-variance [12] of the uniform distributed noise.…”

Section: Noisementioning

confidence: 99%

Privacy Preserving Techniques Applied to CPNI Data: Analysis and Recommendations

Murray,

Mashhadi,

Lagesse

et al. 2021

Preprint

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With mobile phone penetration rates reaching 90%, Consumer Proprietary Network Information (CPNI) can offer extremely valuable information to different sectors, including policy makers. Indeed, as part of CPNI, Call Detail Records has been successfully used to provide real-time traffic information, to improve our understanding of the dynamics of people's mobility and so to allow prevention and measures in fighting infectious diseases, and to offer population statistics. While there is no doubt of the usefulness of CPNI data, privacy concerns regarding sharing individuals' data have prevented it from being used to its full potential. Traditional de-anonymization measures, such as pseudonymization and standard de-identification, have been shown to be insufficient to protect privacy. This has been specifically shown on mobile phone datasets. As an example, researchers have shown that with only four data points of approximate place and time information of a user, 95% of users could be re-identified in a dataset of 1.5 million mobile phone users. In this landscape paper we discuss the state-of-the-art anonymization techniques and their shortcomings.

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Utilizing Noise Addition for Data Privacy, an Overview

Cited by 16 publications

References 26 publications

Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

You can't always get what you want: towards user-controlled privacy on Android

Privacy Preserving Techniques Applied to CPNI Data: Analysis and Recommendations

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