Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5273~5281
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5273-5281  5273
Journal homepage: http://ijece.iaescore.com
Performance analysis of perturbation-based privacy preserving
techniques: an experimental perspective
Ritu Ratra, Preeti Gulia, Nasib Singh Gill
Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India
Article Info ABSTRACT
Article history:
Received Sep 2, 2022
Revised Feb 14, 2023
Accepted Feb 27, 2023
Nowadays, enormous amounts of data are produced every second. These
data also contain private information from sources including media
platforms, the banking sector, finance, healthcare, and criminal histories.
Data mining is a method for looking through and analyzing massive volumes
of data to find usable information. Preserving personal data during data
mining has become difficult, thus privacy-preserving data mining (PPDM) is
used to do so. Data perturbation is one of the several tactics used by the
PPDM data privacy protection mechanism. In perturbation, datasets are
perturbed in order to preserve personal information. Both data accuracy and
data privacy are addressed by it. This paper will explore and compare
several hybrid perturbation strategies that may be used to protect data
privacy. For this, two perturbation-based techniques named improved
random projection perturbation (IRPP) and enhanced principal component
analysis-based technique (EPCAT) were used. These methods are employed
to assess the precision, run time, and accuracy of the experimental results.
This paper provides the impacts of perturbation-based privacy preserving
techniques. It is observed that hybrid approaches are more efficient than the
traditional approach.
Keywords:
Differential privacy
Healthcare
Naive Bayes
Perturbation
Principal component analysis
Random projection
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ritu Ratra
Department of Computer Science and Applications, Maharshi Dayanand University
Rohtak, Haryana, India
Email: ritu.rs.dcsa@mdurohtak.ac.in
1. INTRODUCTION
Mining datasets spread across numerous organizations without disclosing further personal
information has recently gained importance. In many businesses, protecting data privacy is currently a big
concern. In present scenario, people are worried that their private information may be exposed and used for
improper purposes. They think that individuals' private information should be protected [1], [2]. Additionally,
safeguards for personal data protection should be in place. Privacy-preserving data mining tools have been
proven and put into practice to solve this issue [3]. Security assurance solutions that have been developed
based on a number of annoyances are being combined using a variety of data mining techniques. Privacy-
preserving data mining (PPDM) helps to safeguard private and sensitive information for individuals.
Description of architectural design of privacy preservation in data mining is shown in Figure 1.
In Figure 1(a), various stages of PPDM process is shown. In order to assure information preservation, this
paper aims to apply perturbation methods [4]. By substituting some alternative data that are similar to those
of records with comparable non-sensitive data, all sensitive data is replaced. It can be carried out using either
the distributions of sensitive data when specific non-sensitive data are present the mean of sensitive data [5],
[6]. These transformations and conversions typically include numerical data. Additionally, some procedures
entail simple changes [7]. By transforming user input into an improbable and unpredictable form,

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 5273-5281
5274
perturbation techniques have been created as a way to ensure secrecy [8]. The data can only be modified by
authorized individuals, as illustrated in the Figure 1(b). The owner then makes the data available to analysts
for the data mining process [9].
(a)
(b)
Figure 1. Description of architectural design of privacy preservation in data mining (a) stages of PPDM and
(b) framework of privacy preserving
Privacy and security of datasets have been the subject of extensive investigation. PPDM strategies
have been proposed and used in a variety of ways. However, the majority of these strategies do not work in
all situations. Mehta and Rao [10] identified existing ways from the field of natural language processing
(NLP) to transform the unstructured data to a structured form. A perturbation-based method for protecting
privacy in data mining was presented by Mariammal [11]. It is basis is the additive rotation strategy. In this
method, the author calculated the privacy level using the variance of the initial dataset. Banu and Nagaveni
[12] provided a data modification-based method to protect confidentiality in data mining process. Its
foundation is the random rotation technique. They calculated the privacy level using the variance of the
initial dataset. Rao et al. [13] demonstrated that this strategy is more efficient and accessible. They contrasted
their algorithm with the perturbation strategy and demonstrated that their algorithm provided 100% data
usefulness. Mary [14] asserts that the random projection strategy has a higher level of privacy than the other
approaches. The photos can be very well maintained by employing RP. This method makes it possible to
protect data better. It is possible to increase privacy. Ghosh et al. [15] gave a thorough analysis of the
currently employed PPDM approaches and categorized the different data modification techniques.
Javid et al. [16] provided a practical hybrid method for safeguarding the dataset's privacy. For numerical
data, they employ geometric data perturbation, and for categorical data, they use the k anonymization
technique. In their approach, they performed perturbation with randomization (intervals). Pika [17]
investigated a number of data perturbation techniques in healthcare. In data perturbation methods, records'
data values are changed. Their research indicates that the perturbation approach utilized to protect the
confidentiality of original values.
Perturbation approach involves altering the original dataset’s structure or introducing a small
amount of noise to the data. By transforming user input into an improbable and unpredictable form, data
perturbation may be utilized to efficiently employ PPDM. It is one of the often used techniques for protecting
privacy [18]. There are several ways of perturbation. A range of methods, including as noise addition, data
hiding techniques, swapping, and many more, may be used to change the information in datasets [19]. The
probability distribution method and the value distortion method are two strategies of perturbation [20], [21].
In first technique, the data is immediately replaced by the distribution, however in the value distortion
method, the data is directly altered either by using another randomization process or by introducing noise.
Perturbation can be of three types: projection, geometric, and random perturbation [22]. In projection
perturbation, modification is accomplished by changing the dimensions. Data randomly moves in this manner
from high-dimensional to low-dimensional space [23]. In the geometric perturbation approach, perturbation
is performed using a mixture of several techniques, including rotation transformation, translation
transformation, and adding random value [24]–[26].

Int J Elec & Comp Eng ISSN: 2088-8708 
Performance analysis of perturbation-based privacy preserving techniques: an … (Ritu Ratra)
5275
When publishing data, the data owner may employ a variety of privacy-preserving techniques.
Owner can use various perturbation strengths to change the datasets [27]. This research offered a comparison
of approaches based on random projection and principal component analysis that concurrently improve data
classification accuracy while lowering the high dimension to a low dimension in order to safeguard the
dataset's privacy [28], [29]. The performance of two perturbation-based privacy-preserving methods is
examined in the current research. Healthcare datasets have been used to test this analysis. The following are
this paper's main contributions: i) The present research provides an exhaustive analysis of PPDM techniques
based on perturbation; ii) Experimental and comparative analysis of two perturbation-based privacy-
preserving methods i.e., improved random projection perturbation (IRPP) and enhanced principal component
analysis-based technique (EPCAT) are described in this paper; and iii) The impact of hybrid privacy
preserving approaches is analyzed in this research.
The further flow of the paper is organized as: research method used in this research is presented in
section 2. Section 3 provides the description of the experimental results and its comparisons with the existing
work. Section 4 concludes the paper in the end.
2. METHOD
Numerous PPDM-related methods and techniques have previously been created and used [30]. In
this article two hybrid techniques named enhanced principal component analysis-based technique and
improved random projection perturbation are discussed. The efficiency of perturbation-based
datasets transformation is also investigated via comparative analysis. These techniques are as follows:
2.1. Enhanced principal component analysis-based technique
It is a principal component analysis (PCA) and classification-based approach that protects privacy.
In this method, the initial stage involves pre-processing the original data using a data filter. The filtered data
is then subjected to a PCA-based modification after the data pre-processing stage. Finally, the modified data
is subjected to a classification approach for data mining.
The following two phases make up the full structure of this technique:
a. Phase 1: The preservation of individual privacy in datasets is the focus of this phase. This phase consists
of mainly two parts. Which are: i) The most crucial component for improving the precision and speed of
the classification approach is the classification filter module (CFM). Prior to the PCA modifications of
the dataset, this filter is applied to the original dataset and ii) The second module is the perturbation
module, where the altered data set is once more disturbed using PCA-based transformations. Additionally
examined and contrasted with the original dataset is the affected dataset's correctness.
b. Phase 2: The perturbed data set is mined after the two aforementioned modules. The “naive Bayes”
approach is used as the classification method in this instance. Additionally, accuracy is calculated on the
original datasets and contrasted with the accuracy of the perturbed dataset. Figure 2 shows the functional
flow diagram for this model.
Figure 2. Representation of framework enhanced principal component analysis-based technique

 ISSN: 2088-8708
5276
2.2. Improved random projection perturbation
Random projection is a potent method that involves utilizing a random 𝑘 × 𝑑 matrix to project the
original high d-dimensional data onto a smaller k-dimensional subspace [31]. Figure 3 provides a general
overview of the design view of improved random projection perturbation method. The technique's overall
structure is split into two sections.
a. Phase 1: The preservation of individual privacy in datasets is the focus of this phase. This phase consists
of two parts: i) Feature selection: This module is used to choose features and improve the classification
technique's accuracy. Prior to the dataset's changes using random projection, this was done to the original
dataset using PCA. Prior to the random projection process and the classification phase, feature selection is
used. In this paper, a feature selection method based on PCA is applied for selection of appropriate
features and ii) Random projection: The perturbed data is adjusted once more in this module utilizing
dimension reduction, which is accomplished with the aid of the random projection method. The datasets
are distorted using the random projection technique. Additionally examined and contrasted with the
original dataset is the affected dataset's correctness.
b. Phase 2: In this phase, perturbed data sets are mined using a particular classification technique. Naïve
Bayes classifier is used in this instance. Additionally, several matrices are computed on the original
datasets and their accuracy is contrasted with that of the perturbed dataset.
Figure 3. Architectural design of improved random projection perturbation
2.3. Simulation
WEKA and R-studio software are used to carry out the performance analysis of both techniques
[32]. The original datasets are used in these experimental analysis together with the chosen methods to create
the transformed datasets [33]. On both datasets, numerous metrics including correctly classified instances,
incorrectly classified instances, true positive (TP) rate, F-measures, model building time are calculated.
These metrics are used to assess how well the chosen algorithms performed on the projected dataset. The
naive Bayes classification method used for implementations of the algorithms in order to evaluate the
efficacy of the strategies.
2.3.1. Naïve Bayes
A number of classification algorithms are involved in naive Bayes classifier. These are based on
Bayes' theorem. Mathematically, Bayes' theorem is stated as (1):
P (AB) =
P (BA) P(A)
𝑃(𝐵)
(1)
where, 𝐴 and 𝐵 are events and 𝑃 (𝐴𝐵) is the probability of event 𝐴 occurring, while 𝐵 has already occurred,
𝑃 (𝐵𝐴) determines the probability of event B occurring, while A has already occurred, 𝑃 (𝐴) defines the
probability of event A occurring. 𝑃 (𝐵) defines the Probability of event B occurring.

5277
In this performance evaluation, classification accuracy and time usage are the two key emphasis
points. Comparisons are made between the performance results and the results collected from the NB. Two
datasets were perturbed by the IRPP and EPCAT. The entire 10-fold cross-validation process used in the
techniques. For the purpose of validation, ten samples were chosen ten times this validation process was
repeated. Nine samples were collected for training in a single run. One sample was utilized to evaluate the
effectiveness of the suggested method. The average accuracy of the 10 iterations was then used to represent
the overall performance for a particular dataset. Figure 4 depicted the snapshots of principal components of
hypothyroid dataset after performing PCA based transformations on training set and test set.
Figure 5 depicts the transformed datasets after perturbation for the cardiovascular system in
Figure 5(a) and for the hypothyroid dataset in Figure 5(b). Modified datasets are shown to be more secure
than the original dataset since it is challenging to access the changed data. So, confidentiality of datasets is
maintained.
Figure 4. Snapshots of principal components of cardiovascular dataset on training set and test set after
EPCAT perturbation technique in R-studio
(a) (b)
Figure 5. Snapshots of datasets after implementation of IRPP perturbation technique in WEKA tool
(a) cardiovascular dataset and (b) hypothyroid dataset
2.3.2. Evaluation metrics
The effectiveness of the used techniques is assessed using various categorization measures. These
include F-measures, TP rate, accuracy, runtime, and false positive (FP) rate.
- Accuracy: Accuracy can be used to evaluate a classification model. It is one aspect that may be
considered to rate a classification models. Accuracy is the proportion of forecasts that our model
successfully predicted. The official definition of accuracy is as (2).
Number of correct predictions
Total number of predictions
(2)

 ISSN: 2088-8708
5278
- TP rate (accuracy +): It is the fraction of accurate forecasts in positive class predictions. Its threshold is
set by default to 80%. The measure is becoming better, as seen by its rising trend. Its descending trend
suggests that the measure is getting worse. Data from feedback is increasingly diverging from training
data. Irregular variance shows the inconsistency of feedback data. The true positive rate is calculated by
(3).
Number of True Positives
(Number of True Positives + Number of False Negatives)
(3)
- FP rate (accuracy-): Accuracy of machine learning algorithms may be evaluated using a statistic called
the False Positive Rate. The false positive rate is determined as (4).
FP
(FP+TP)
(4)
- F-measures: It is combined measure for precision and recall metrics. It provides a single score that
balances both the concerns of precision and recall in one number. It is calculated as (5).
2 ∗ Precision ∗ Recall
(Precision + Recall)
(5)
3. RESULTS AND DISCUSSION
This research analyses the impact of hybrid perturbation based PPDM techniques in data mining
process. For this purpose, IRPP and EPCAT are selected. Several tests were run on data sets of two different
sizes, and the associated outcomes were seen. The results of the experiments demonstrate that the both hybrid
techniques IRPP and EPCAT perform better due to their greater accuracy, TP rate, FP rate, F-measures, and
run duration values.
- Datasets: This research is implemented and experimented on two datasets i.e., cardiovascular dataset and
the hypothyroid dataset. The cardiovascular dataset consists of 70k instances and 13 attributes. The
hypothyroid dataset consists of 7,200 instances and 21 attributes [34].
The effectiveness of both method on cardiovascular datasets is shown in Table 1. On the provided
training datasets, the metrics accuracy, TP rate, FP rate, F-measures, and run time are computed. It is clearly
shown in the Table 1 and can be noticed that the strategies produced superior results than the conventional
model of categorization in all regards.
Table 1. Performance measure of naïve Bayes classifier on cardiovascular dataset
Accuracy measurement Original dataset IRPP based perturbed dataset EPCAT based perturbed dataset
Time taken to build (sec) 0.53 0.34 0.25
Correctly classified instances (%) 58.85 59.87 59.65
Incorrectly classified instances (%) 41.15 40.13 40.31
TP rate (%) 58.90 59.70 59.16
F-measures (%) 54.40 56.50 56.14
The performance of IRPP and EPCAT privacy-preserving algorithms to the conventional
classification algorithms on cardiovascular datasets is shown in Figure 6. It displays the efficiency of the
random projection-based privacy-preserving and PCA-based privacy-preserving method to the traditional
classification model on cardiovascular datasets. As shown in the figure, it is observed that the IRPP method
has better accuracy measures than the conventional classification algorithms and PCA-based privacy-
preserving method. The effectiveness of both methods to the conventional classification model on
hypothyroid dataset is shown in Table 2.
On provided training datasets, the metrics accuracy, TP rate, FP rate, F-measures, and run time are
calculated. It is well depicted in the in the table, and it is easy to see that the IRPP approach yields better
results overall than the conventional model of classification. On hypothyroid datasets, Figure 7 compares the
efficiency of the privacy-preserving algorithms IRPP and EPCAT to the traditional classification techniques.
It demonstrates the effectiveness of the privacy-preserving random projection and PCA methods in
comparison to the conventional classification model on hypothyroid datasets. As depicted in the Figure 7, it
can be seen that the IRPP approach outperforms both the PCA-based privacy-preserving method and
traditional classification algorithms in terms of accuracy measurements.

5279
Figure 6. Performance analysis IRPP and EPCAT to the conventional model on cardiovascular dataset
Table 2. Performance measure of naïve Bayes classifier on hypothyroid dataset
Accuracy measurement Original dataset IRPP based perturbed dataset EPCAT based perturbed dataset
Correctly classified instances (%) 71.67 75.67 74.08
Incorrectly classified instances (%) 28.32 24.32 25.91
TP rate (%) 71.70 75.13 74.10
F-Measures (%) 70.80 73.54 73.48
Figure 7. Performance Analysis IRPP and EPCAT to the conventional model on hypothyroid dataset using
naïve Bayes classifier
4. CONCLUSION
Developing algorithms that can conceal or provide privacy to some sensitive information is the
fundamental goal of privacy preservation in data mining operations. PPDM techniques are essential in order
to stop profiteers from gaining unwanted access. However, data mining accuracy and privacy conflict. In this
context, this paper has analyzed the impact of PPDM techniques based on perturbation to datasets. This
article provides a brief overview of some privacy techniques, namely PCA-based perturbation, and random
projection-based perturbation, and analyzes their competencies and differences in different scenarios. The
effectiveness of these hybrid techniques has been tested in classification algorithms naive Bayes classifiers.
For implementation purpose two datasets cardiovascular and hypothyroid datasets have been selected. It has
been found that IRPP privacy-preserving approach and enhanced principal component analysis-based
technique EPCAT are more effective than traditional technique. The perturbed datasets are more privacy
preserved. In cardiovascular dataset’s case, the perturbed datasets outperform the original dataset in terms of
runtime, accuracy, TP rate, and F-measurer. In hypothyroid dataset’s case, implementation results on all
measurements are better or almost identical to the previous approach model. Therefore, it is noticed that the
datasets that are altered using hybrid privacy preserving approaches are more secure and efficient than the
original datasets.
0
10
20
30
40
50
60
70
Correctly Classified
Instances (%)
Incorrectly Classified
Instances (%)
TP Rate (%) F-Measures (%)
No.
of
Instances
(%)
Performance Metrics
Original dataset
IRPP based Perturbed dataset
EPCAT based Perturbed
dataset
0
10
20
30
40
50
60
70
80
Correctly Classified
Instances (%)
Incorrectly Classified
Instances (%)
TP Rate (%) F-Measures (%)
No.
of
Instances
Performance Metrics
Original dataset
IRPP based Perturbed
dataset
EPCAT based Perturbed
dataset

 ISSN: 2088-8708
5280
REFERENCES
[1] A. Altalhi, M. AL-Saedi, H. Alsuwat, and E. Alsuwat, “Privacy-preserving in the context of data mining and deep learning,”
International Journal of Computer Science and Network Security, vol. 21, no. 6, pp. 137–142, 2021.
[2] M. T. Andavan and N. Vairaperumal, “Privacy protection domain-user integra tag deduplication in cloud data server,”
International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 4, pp. 4155–4163, Aug. 2022, doi:
10.11591/ijece.v12i4.pp4155-4163.
[3] P. Gulia and Hemlata, “Privacy preserving data mining of vertically partitioned data in distributed environment-an experimental
analysis,” Journal of Theoretical and Applied Information Technology, vol. 96, no. 10, pp. 2973–2987, 2018.
[4] R. Ratra and P. Gulia, “Privacy preserving data mining: techniques and algorithms,” International Journal of Engineering Trends
and Technology, vol. 68, no. 11, pp. 56–62, Nov. 2020, doi: 10.14445/22315381/IJETT-V68I11P207.
[5] H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar, “Random-data perturbation techniques and privacy-preserving data mining,”
Knowledge and Information Systems, vol. 7, no. 4, pp. 387–414, May 2005, doi: 10.1007/s10115-004-0173-6.
[6] Kun Liu, H. Kargupta, and J. Ryan, “Random projection-based multiplicative data perturbation for privacy preserving distributed
data mining,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 92–106, Jan. 2006, doi:
10.1109/TKDE.2006.14.
[7] G. N. Devi and K. Manikandan, “Improved perturbation technique privacy‐preserving rotation‐based condensation algorithm for
privacy preserving in big data stream using internet of things,” Transactions on Emerging Telecommunications Technologies,
vol. 31, no. 12, Dec. 2020, doi: 10.1002/ett.3970.
[8] K. M. Chong, “Privacy-preserving healthcare informatics: a review,” ITM Web of Conferences, vol. 36, Jan. 2021, doi:
10.1051/itmconf/20213604005.
[9] M. Dabhade and J. J. Hilda, “Privacy preserving in data mining using data perturbation and classification method,” Emerging
trends in Computer Engineering and Research, vol. 8, no. 2, pp. 346–352, 2017.
[10] B. B. Mehta and U. P. Rao, “Improved l-diversity: scalable anonymization approach for privacy preserving big data publishing,”
Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1423–1430, Apr. 2022, doi:
10.1016/j.jksuci.2019.08.006.
[11] S. Mariammal, “An additive rotational perturbation technique for privacy preserving data mining,” Turkish Journal of Computer
and Mathematics Education (TURCOMAT), vol. 12, no. 9, pp. 2675–2681, 2021.
[12] R. V. Banu and N. Nagaveni, “Preservation of data privacy using PCA based transformation,” in 2009 International Conference
on Advances in Recent Technologies in Communication and Computing, 2009, pp. 439–443, doi: 10.1109/ARTCom.2009.159.
[13] P. R. M. Rao, S. M. Krishna, and A. P. S. Kumar, “Novel algorithm for efficient privacy preservation in data analytics,” Indian
Journal of Science and Technology, vol. 14, no. 6, pp. 519–526, Feb. 2021, doi: 10.17485/IJST/v14i6.1773.
[14] Av. Mary, “A random projection approach to secure medical images,” International Journal of Advanced Research, vol. 7, no. 3,
pp. 1298–1301, Mar. 2019, doi: 10.21474/IJAR01/8763.
[15] S. Ghosh, S. Sadhu, S. Biswas, D. Sarkar, and P. P. Sarkar, “A comparison between different classifiers for tennis match result
prediction,” Malaysian Journal of Computer Science, vol. 32, no. 2, pp. 97–111, Apr. 2019, doi: 10.22452/mjcs.vol32no2.2.
[16] T. Javid, M. K. Gupta, and A. Gupta, “A hybrid-security model for privacy-enhanced distributed data mining,” Journal of King
Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 3602–3614, Jun. 2022, doi:
10.1016/j.jksuci.2020.06.010.
[17] A. Pika, M. T. Wynn, S. Budiono, A. H. M. Ter Hofstede, W. M. P. van der Aalst, and H. A. Reijers, “Privacy-preserving process
mining in healthcare,” International Journal of Environmental Research and Public Health, vol. 17, no. 5, p. 1612, Mar. 2020,
doi: 10.3390/ijerph17051612.
[18] R. Ratra, P. Gulia, and N. S. Gill, “Evaluation of re-identification risk using anonymization and differential privacy in healthcare,”
International Journal of Advanced Computer Science and Applications, vol. 13, no. 2, pp. 563–570, 2022, doi:
10.14569/IJACSA.2022.0130266.
[19] T. A. Adesuyi and B. M. Kim, “A layer-wise Perturbation based Privacy Preserving Deep Neural Networks,” in 2019
International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Feb. 2019, pp. 389–394, doi:
10.1109/ICAIIC.2019.8669014.
[20] R. Ratra, P. Gulia, N. S. Gill, and J. M. Chatterjee, “Big data privacy preservation using principal component analysis and
random projection in healthcare,” Mathematical Problems in Engineering, vol. 2022, pp. 1–12, Aug. 2022, doi:
10.1155/2022/6402274.
[21] A. Amkor, K. Maaider, and N. El Barbri, “An evaluation of machine learning algorithms coupled to an electronic olfactory
system: a study of the mint case,” International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 4,
pp. 4335–4344, Aug. 2022, doi: 10.11591/ijece.v12i4.pp4335-4344.
[22] E. O. Abiodun, A. Alabdulatif, O. I. Abiodun, M. Alawida, A. Alabdulatif, and R. S. Alkhawaldeh, “A systematic review of
emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities,”
Neural Computing and Applications, vol. 33, no. 22, pp. 15091–15118, Nov. 2021, doi: 10.1007/s00521-021-06406-8.
[23] S. A. Abdelhameed, S. M. Moussa, N. L. Badr, and M. E. Khalifa, “The generic framework of privacy preserving data mining
phases: challenges & future directions,” in 2021 Tenth International Conference on Intelligent Computing and Information
Systems (ICICIS), Dec. 2021, pp. 341–347, doi: 10.1109/ICICIS52592.2021.9694174.
[24] X. Fan, G. Wang, K. Chen, X. He, and W. Xu, “PPCA: privacy-preserving principal component analysis using secure multiparty
computation (MPC),” arXiv:2105.07612, May 2021.
[25] Z. Chen and K. Omote, “A privacy preserving scheme with dimensionality reduction for distributed machine learning,” in 2021
16th Asia Joint Conference on Information Security (AsiaJCIS), Aug. 2021, pp. 45–50, doi: 10.1109/AsiaJCIS53848.2021.00017.
[26] M. A. Abdo, A. A. Abdel-Hamid, and H. A. Elzouka, “A cloud-based mobile healthcare monitoring framework with location
privacy preservation,” in 2020 International Conference on Innovation and Intelligence for Informatics, Computing and
Technologies (3ICT), Dec. 2020, pp. 1–8, doi: 10.1109/3ICT51146.2020.9311999.
[27] M. B. Malik, M. A. Ghazi, and R. Ali, “Privacy preserving data mining techniques: current scenario and future prospects,” in
2012 Third International Conference on Computer and Communication Technology, Nov. 2012, pp. 26–32, doi:
10.1109/ICCCT.2012.15.
[28] P. H. Li, T. Lee, and H. Y. Youn, “Dimensionality reduction with sparse locality for principal component analysis,” Mathematical
Problems in Engineering, vol. 2020, pp. 1–12, May 2020, doi: 10.1155/2020/9723279.
[29] T. Meurers, R. Bild, K.-M. Do, and F. Prasser, “A scalable software solution for anonymizing high-dimensional biomedical data,”
GigaScience, vol. 10, no. 10, Oct. 2021, doi: 10.1093/gigascience/giab068.

5281
[30] P. Churi, A. Pawar, and A.-J. Moreno-Guerrero, “A comprehensive survey on data utility and privacy: taking Indian healthcare
system as a potential case study,” Inventions, vol. 6, no. 3, Jun. 2021, doi: 10.3390/inventions6030045.
[31] T. Bianchi, V. Bioglio, and E. Magli, “Analysis of one-time random projections for privacy preserving compressed sensing,”
IEEE Transactions on Information Forensics and Security, vol. 11, no. 2, pp. 313–327, 2016, doi: 10.1109/TIFS.2015.2493982.
[32] R. Ratra and P. Gulia, “Experimental evaluation of open source data mining tools (WEKA and orange),” International Journal of
Engineering Trends and Technology, vol. 68, no. 8, pp. 30–35, Aug. 2020, doi: 10.14445/22315381/IJETT-V68I8P206S.
[33] S. K. David, M. Rafiullah, and K. Siddiqui, “Comparison of different machine learning techniques to predict diabetic kidney
disease,” Journal of Healthcare Engineering, vol. 2022, pp. 1–9, Apr. 2022, doi: 10.1155/2022/7378307.
[34] “Find open datasets and machine learning projects,” Kaggle. Accessed: May 31, 2022. [Online]. Available:
https://www.kaggle.com/datasets.
BIOGRAPHIES OF AUTHORS
Ritu Ratra received the MCA degrees from Maharshi Dayanand University,
Rohtak, and Haryana, India. Currently, she is pursuing her Ph.D. in Computer Science at the
Department of Computer Science and Applications, Maharshi Dayanand University. Rohtak,
Haryana, India. Previously, she had worked as an Assistance Professor at Sh. Lal Nath Hindu
College, Rohtak, Haryana, India affiliated with MD University, Rohtak, and Haryana, India
for 12.5 years. Her research areas include machine learning, data mining, big data, and
artificial intelligent. She has authored or coauthored many refereed journal and conference
papers. She can be contacted at email: ritu.rs.dcsa@mdurohtak.ac.in.
Preeti Gulia received Ph.D. degree in computer science in 2013. She is currently
working as Associate Professor at the Department of Computer Science and Applications,
M.D. University, Rohtak, Haryana, India. She is serving the Department since 2009. She has
published more than 65 research papers and articles in journals and conferences of National/
International repute including ACM, and Scopus. Her area of research includes data mining,
big data, machine learning, deep learning, IoT, and software engineering. She is an active
professional member of IAENG, CSI, and ACM. She is also serving as editorial board
member active reviewer of international/national journals. She has guided four research
scholars as well as guiding six Ph.D. research scholars from various research areas. She can be
contacted at email: preeti@mdurohtak.ac.in.
Nasib Singh Gill holds Post-Doctoral research in Computer Science at Brunel
University, West London during 2001-2002 and Ph.D. in Computer Science in 1996. He is a
recipient of the Commonwealth Fellowship Award of the British Government for the Year
2001. Besides, he also has earned his MBA degree. He is currently Head, Department of
Computer Science and Applications, M. D. University, Rohtak, India. He is also working as
Director, Directorate of Distance Education as well as Director of Digital Learning Centre, M.
D. University, Rohtak, Haryana. He is an active professional member of IETE, IAENG, and
CSI. He has published more than 304 research papers and authored 5 popular books He has
guided so far 12 Ph.D. scholars as well as guiding about 5 more scholars. His research
interests primarily include-IoT, machine and deep learning, information and network security,
data mining and data warehousing, NLP, and measurement of component-based systems. He
can be contacted at email: nasib.gill@mdurohtak.ac.in.

Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

More Related Content

Similar to Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective (20)

More from IJECEIAES (20)

Recently uploaded (20)

Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective