A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Using Machine Learning Classification as a Gauge

Mivule, Kato; Turner, Claude

doi:10.1016/j.procs.2013.09.295

Cited by 21 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Filtered x-CEG, an adaptation of the Comparative x-CEG heuristic model outlined in Mivule and Turner (2013), is suggested [6]. Signal processing techniques, such as, discrete cosine transforms are used in the Filtered x-CEG, illustrated in Figure 1, unlike the model in [6], that does not involve signal processing methods [17].…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, usability, would be a measure of the closeness between the original and privatized data [7]. However, in this study, the classification error is used as a gauge for data privacy and usability quantification [6]. On the subject of discrete cosine transforms and data privacy, studies have mostly been done in the image and audio processing areas, with focus on access control instead of confidentiality [8] [9][10] [11].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Therefore, as a contribution, the Filtered Classification Error Gauge (Filtered x-CEG) methodology is suggested as a heuristic for the generation of privatized synthetic data [17]. The Filtered x-CEG is a variation of the Comparative x-CEG heuristic process described in Mivule and Turner (2013) [6] and [17]. The Filtered x-CEG heuristic works as follows: (i) Data privacy is applied to the data using noise addition; (ii) in the second step, signal processing technique of discrete cosine transforms, is used to mine the coefficients; (iii) the coefficients are added back to the noisy data; (iv) new privatized synthetic data is produced with a similar formation as the original [17]; (v) the moving average filter is then applied to the privatized synthetic data to improve usability; (vi) machine learning classification is used to test the filtered synthetic data for usability, with lower classification error (high classification accuracy) as an indication of better data usability [6] [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Study of Privatized Synthetic Data Generation Using Discrete Cosine Transforms

Mivule¹

2014

ijacsa

Self Cite

View full text Add to dashboard Cite

Abstract-In order to comply with data confidentiality requirements, while meeting usability needs for researchers, entities are faced with the challenge of how to publish privatized data sets that preserve the statistical traits of the original data. One solution to this problem, is the generation of privatized synthetic data sets. However, during data privatization process, the usefulness of data, have a propensity to diminish even as privacy might be guaranteed. Furthermore, researchers have documented that finding an equilibrium between privacy and utility is intractable, often requiring trade-offs. Therefore, as a contribution, the Filtered Classification Error Gauge heuristic, is presented. The suggested heuristic is a data privacy and usability model that employs data privacy, signal processing, and machine learning techniques to generate privatized synthetic data sets with acceptable levels of usability. Preliminary results from this study show that it might be possible to generate privacy compliant synthetic data sets using a combination of data privacy, signal processing, and machine learning techniques, while preserving acceptable levels of data usability.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study of Privatized Synthetic Data Generation Using Discrete Cosine Transforms

Mivule¹

2014

ijacsa

Self Cite

View full text Add to dashboard Cite

show abstract

“…Perfect utility can be achieved by publishing data as received but this has no privacy at all. In (Mivule & Turner, 2013), the more confidential data is , the more likely that the privatized data will decline in utility and therefore may become useless. (Doka et al, 2015) argue that utility may be achieved at the expense of runtime since the anonymization process is a one-time process.…”

Section: Data Utility Vs Privacymentioning

confidence: 99%

Feature Based Data Anonymization for High Dimensional Data

Gachanga¹,

Kimwele²,

Nderu³

2019

JIEA

View full text Add to dashboard Cite

Information surges and advances in machine learning tools have enable the collection and storage of large amounts of data. These data are highly dimensional. Individuals are deeply concerned about the consequences of sharing and publishing these data as it may contain their personal information and may compromise their privacy. Anonymization techniques have been used widely to protect sensitive information in published datasets. However, the anonymization of high dimensional data while balancing between privacy and utility is a challenge. In this paper we use feature selection with information gain and ranking to demonstrate that the challenge of high dimensionality in data can be addressed by anonymizing attributes with more irrelevant features. We conduct experiments with real life datasets and build classifiers with the anonymized datasets. Our results show that by combining feature selection with slicing and reducing the amount of data distortion for features with high relevance in a dataset, the utility of anonymized dataset can be enhanced.

show abstract

“…Moreover, the significant amount of randomization produced by certain DP algorithms results in low data utility. Existing perturbation mechanisms often ignore the connection between utility and privacy, even though improvement of one leads to deterioration of the other [32]. Furthermore, the inability to efficiently process high volumes of data and data streams makes the existing methods unsuitable for privacy-preservation in smart cyber-physical systems.…”

Section: Introductionmentioning

confidence: 99%

An efficient and scalable privacy preserving algorithm for big data and data streams

Chamikara

Bertók

Liu

et al. 2019

Computers & Security

View full text Add to dashboard Cite

The published article can be found at https://doi.A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset. few years. These systems often interact with the environment to collect data mainly for analysis, e.g. to allow life activities to be more intelligent, efficient, and reliable [1]. Such data often includes sensitive details, but sharing confidential information with third parties can lead to a privacy breach.From our perspective, privacy can be considered as "Controlled Information Release" [2]. We can define a privacy breach as the release of private/confidential information to an untrusted environment.However, sharing the data with external parties may be necessary for data analysis, such as data mining and machine learning. Smart cyber-physical systems must have the ability to share information while limiting the disclosure of private information to third parties. Privacy-preserving data sharing and privacy-preserving data mining face significant challenges because of the size of the data and the speed at which data are produced. Robust, scalable, and efficient solutions are needed to preserve the privacy of big data and data streams generated by SCPS [3,4]. Various solutions for privacy-preserving data mining (PPDM) have been proposed for data sanitization; they aim to ensure confidentiality and privacy of data during data mining [5,6,7,8].The two main approaches of PPDM are data perturbation [9, 10] and encryption [11,12]. Although encryption provides a strong notion of security, due to its high computation complexity [13] it can be impractical for PPDM of SCPS-generated big data and data streams. Data perturbation, on the other hand, applies certain modifications such as randomization and noise addition to the original data to preserve privacy [14]. These modification techniques are less complex than cryptographic mechanisms [15]. Data perturbation mechanisms such as noise addition [16] an...

show abstract

A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Using Machine Learning Classification as a Gauge

Cited by 21 publications

References 13 publications

A Study of Privatized Synthetic Data Generation Using Discrete Cosine Transforms

A Study of Privatized Synthetic Data Generation Using Discrete Cosine Transforms

Feature Based Data Anonymization for High Dimensional Data

An efficient and scalable privacy preserving algorithm for big data and data streams

Contact Info

Product

Resources

About