Vol: 8 Issue: 4
Published In: October 2019
Article No: 3 Page: 439446 doi: https://doi.org/10.13052/jcsm22451439.843
PSVGWO: Particle Swarm Velocity Aided GWO for Privacy Preservation of Data
Jyothi Mandala^{1,2,*} and Dr. M. V. P. Chandra Sekhara Rao^{3}
^{1} Research Scholar, ANU, Guntur, Andhra Pradesh 522019, India
^{2}Assistant Professor, GMRIT, Rajam, Andhra Pradesh 532127, India
^{3} Professor, RVR & JC College of Engineering, Guntur, Andhra Pradesh 522019, India
Email: jyothirajb4u@gmail.com
* Corresponding Author
Received 09 June 2018; Accepted 21 September 2018; Publication 20 June 2019
Due to the maximum usage of Social Networking Sites (SNS) the number of individuals that are posting their health information online is increasing. The health information of the user’sis disclosed on these sites, where the organization or various individuals can mine that for numerous research and commercial purposes. Because of this sensitive nature of the medical information, the privacy protection is said to be a main focus for the researchers. On analyzing many of the conventional methods, there is an improvement in the sanitization process but still lacks on the restoration of data. Thus, this paper focused on the privacy preservation over the healthcare records. The proposed model is about the enhancement in the sanitization technique that hides the raw information presented by the users. The sanitization process involves the generation of key that created optimally by introducing a new Particle Swarm Velocity aided GWO (PSVGWO) algorithm. Additionally, the authorized user can restore these sanitized medical data securely. Finally, the traditional algorithms are compared with the proposed model in terms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Crow Search Optimization (CSA) and Adaptive Awareness Probabilitybased CSA (AAPCSA) and the outcome is analyzed.
Keywords: Healthcare Data Preservation, Data Restoration, Sanitized Data, Key generation, Modified Optimization.
The large quantities of data that are extracted from the unknown previously interesting patterns by using the automatic and semiautomatic analysis are said to be the task in data mining. The data mining [9, 11] includes a collection of unusual records (anomaly detection); dependencies (association rule mining) and similar data records (cluster analysis). Generally, the task in data mining is divided into two phases: descriptive and predictive. The general properties of data are characterized by the Descriptive mining in the database. The task of Predictive mining was to execute the current data inference thereby predictions are made.
The user data in social network sites of entire kind, i.e., the search engines and shopping sites can be further utilized and analyzed by data mining [15, 17] in organizations and individuals. The raw data is unavoidably in revealing and privacy leakage can occur at the time of this process, because of the use of private and sensitive information. Diversely, in publishing applications of many data that presented the data directly to the users in database, the data protection has to be made by the data publishers, or it will lead to the leakage in sensitivity data. Hence, the privacy has to be provided without any compromise in significant accuracy of data mining [19] by using privacy protection technique [20]; this is the major challenge in the data mining. For science and business purposes the ATA mining is used widely. The data that are collected by the individuals or information providers are the major one for pattern recognition or decision making.
Data encryption, data distortion, and limited data publishing, etc. are involved in the existed privacy preservation [12, 13] strategy. The encryption technique is accepted in the mining data procedure by data encryption for hiding the data that is sensitive that is utilized often in the dispersed environment. The data is published provisionally on definite conditions by the limited data publishing thereby the path of publishing definite values of data, anonymizing or generalizing the data, and so on. In privacy protection [14], the sensitive data is distorted by using the Data distortion strategy when maintaining the data attributes or some data is integrated by the addition of noise, blocking, making exchange and randomization and so on. The processed data can be making sure to protect the definite statistics properties in mining data and for additional process.
To build the algorithmic scale and to attain a larger accuracy, when managing the guaranteed privacy is the major challenge in Privacy preservation in data mining (PPDM) [10, 30]. The existed methods and definitions of privacy are not suitable for the PPDM [16, 18, 29] techniques. Additional statements will lead to the low computational cost and help to acquire better accuracy. Big data is taken into consideration because of its risk process. This involves the lifecycle of information, collection process, and data creation, and also the requirement in security process. The objective of this big data security is the same as that of the previous methods. They are to protect the availability, confidentiality, and integrity.
The proposed privacy preservation model implements the improvement of sanitized method for hiding the raw data that are offered by the users. The key generation is involved in this sanitized process. This paper introduces a new algorithm namely PSVGWO to find the optimal key. Further, the sanitized medical data is restored effectively by the authorized user. At last, the proposed method is compared over the traditional algorithms like PSO, GA, DE, CSA and AAPCSA and the resultant outcome is analyzed. The organization of this paper is as follows: Section 2 explains the Literature review. Section 3 analyses the modeling of medical data privacy preservation. The optimal key extraction strategy is described in Section 4. Section 5 explains the result and discussion work of this paper, and finally, Section 6 concludes the paper.
In 2016, Li et al. [1] have implemented two dispersed privacypreserving protocols that have been based on the distributed ensemble techniques. The main impact of the implemented technique was for the purpose of learning the data distribution, to outline an elegant approach more accurately. Further to transmit the achieved healthcare knowledge not by showing and sharing the sensitive data of client or patient, and thereby privacy of the patient has been protected. They have verified the implemented model has an effective performance in accuracy as well as in the time robust prediction techniques. The implemented model performance has estimated with the help of type2 diabetes ‘electronic health records EHRs’, which was gathered from numerous sources. Further, with the assist of implemented model, they can found the fundamental biomarkers (both universal as well as regionspecific), and also have certified the chosen biomarkers by biomedical literature.
In 2018, Ni et al. [2] have implemented a Differential Privacy Preservation Multiple Cores DBSCAN Clustering (DPMCDBSCAN) schema on the basis of the differential privacy that was powerful as well as by the algorithm named DBSCAN for effectively influence the privacy leakage problems for the user data network within the procedure of mining the data, and to improve the efficiency in the data clustering thereby in addition of Laplace noise. The wide theoretical review and simulations were performed to estimate that the resultant structure has shown enhanced accuracy, efficiency, and privacy preservation result when compared with the conventional structures.
In 2017, Gao et al. [3] have developed an arrative reversible data hiding (RDH) algorithm mainly for medical images. The major aim of this implemented algorithm was to achieve a divergence enhancement in ‘region of interest (ROI)’ without any distortion and thereby accomplished interfere localization above assault in ROI. At first, the background and the ROI of consequent image were segmented with the assist of a threshold method ‘Otsu’s’. Moreover, a better technique was applied for preprocessing the diminishing intention of visual distortion. The implemented method was comparing with other conventional models, which show that Experiment was estimated the dominance over implemented algorithm corresponding to an enhancement of contrast in ROI, also to safeguard the quality of visual and location of tamper.
In 2017, Zhang et al. [4] have developed a new privacypreserving decision tree classification construction model on the basis of various privacy protection methods, to review the issues in the privacy disclosure at the time of data mining. The feedback that was used in efficient classifier was divided into two various noises through exponential mechanisms and Laplace, thereby the resultant calculation was disturbed and was presented to a construction algorithm in which a secure data assessment interface was provided to the users. The continuous and discrete values was provided with various split solution and was utilized for the optimization of search structure to minimize the rate of error in classifier. The lower sensitivity quality function was available, thereby chosen to make decisions and to enhance the allocation budget in the privacy method. The personal information that was obtained by the unknown sensitive nodes in tree data type in the potential problem was resolved accordingly. The simulation experiment showed the better accuracy and the privacy protection in the implemented model.
In 2017, Kim et al. [5] have intended the efficiency evaluation and also the proficiency of data cubes (preservation of privacy) in ‘electronic medical records (EMRs)’. EMR statistics became difficult because of these data cubes that were summarized by the entire feasible blends of attributes. The big data was analyzed effectively by these extensive data cubes, which was a large probability in analyzing such as EMR analysis. They have developed a privacypreserving structure for EMR data cube, mainly; the privacy of data has to be attained by using the anonymization models. Moreover, they have paid attention on alterations that happened in privacy preservation by the process of anonymization. Hence virtually evaluated the several types of ‘privacypreserving EMR data cubes’ with the utilization of definite metrics as well as argued about the ability of every anonymization method.
In 2012, Fong and Jahnke [6] had introduced the privacypreserving method, which was used in decision tree learning with no associated accuracy loss. Here the privacy preservation in the gathered data samples was described at times when the sample database information was lost partially. In this method, the sample original datasets were converted into anunreal dataset groups; thereby the sample original datasets was not recreated by not utilizing the total unreal datasets group. At the same time, these unreal datasets directly build the decision tree with clear accuracy. The data storage was applied directly by this approach by how fast the first sample was gathered. This method was suitable for the other conventional methods, like cryptography, thereby promotes additional protection.
In 2017, Poulis et al. [7] have presented a narrative method, which enforced the exact requirement. The efficacy constriction concept was developed for both codes (demographics and diagnosis codes). The generation number can be limited by these efficacy constrictions that in turn defined by data owners. The algorithm was developed in order to appreciate the developed model in which it enforce (k,km)anonymity on a dataset that includes both stated codes; thereby it would convince the exact efficacy constrictions with fewer information loss. The experiment along with a large dataset that involves more than 200,000 ‘electronic health records’ was examined for the effectiveness and proficiency in implemented algorithm.
In 2016, Xu et al. [8] have stated that the researchers required a path for organizing the various ongoing works, to guard the sensitive information in the data mining. The Rampart framework categorizes protection method was implemented for encouraging the interdisciplinary clarification in growth variation of privacy issues connected along the data with knowledge discovery.
The features and challenges of privacy protection in data mining are summarized in Table 1. The methodology along with the features and challenges are as follows: Ensemble learning [1] makes ease in identification of the region definite biomarkers and guides a new innovative clinical area records. But it still possesses some challenges that are it has less accuracy and moreover, the enhancement is needed to attain the idea of anonymity. DPMCDBSCAN [2] the amount of noise added is independent of the dataset scale, and a small amount of noise is needed for the huge datasets. The drawback of this method is less accuracy and influence of the input parameters have to be reduced. Reversible data hiding [3] has an improved ROI contrast, and the redundant shifting process has to be evaded. Unable to find the temper and the contrast enhancement cannot be executed; these are the major challenges in this model. Differential privacy decision tree construction model [4] is entirely independent of any background knowledge, and it is nonsensitive to any data modification in records. The drawbacks are no access to accuracy of potential loss in privacy, and it offers protection only against the single knowledge attack model. Generalization method [5] can access and build the anonymized EMR data cubes, and the features of EMR analysis are measured. The major disadvantage is, there is a difficulty in getting the optimal result, and only the count measures are calculated. Decision tree generation [6] improves the privacy security, and the utility of the sample data is preserved. The challenges that are to be rectified for future use are: the storage size in the unrealized samples has to be optimized and the processing time is low. Generalization method [7] protects large data utility, and it is more efficient. It only thinks about the unordered sets which are the major drawback of this model and cannot be corrected when a setting gets varied. Rampart framework [8] avoids the direct use of sensitive raw data and provides the security in delivering mining results. The limitations it posses are, enhancement needed to control the use of personal information, and there is a risk in covering user’s personal information.
Author  Method  Features  Challenges 
Li et al. [1]  Ensemble learning 


Ni et al. [2]  DPMCDBSCAN 


Gao et al. [3]  Reversible data hiding 


Zhang et al. [4]  Differential privacy decision tree construction model 


Kim et al. [5]  Generalization method 


Fong and Jahnke  Decision Tree Generation 


Pouliset al. [7]  Generalization method 


Xu et al. [8]  Rampart frame work 


The data sanitization and data restoration are the two processes that are involved in the proposed medical data preservation model. At first, sanitization process carried out under sensitive data, and for hiding the sensitive data, a key is created. As the optimal key is the major issue, this paper uses a new PSVGWO for generating optimal key. Hence the secure transmission of the sanitized data in database is done via transmission line, after that it puts to the restoration process from where the medical data that is sanitized was recovered effectively by the authorized user. Here the Figure 1 illustrates the overall architecture model of privacy preservation.
Transactions  Data  
I_{1}  1  2  
I_{2}  1  3  
I_{3}  2  3  4 
I_{4}  1  3  4 
I_{5}  3  4 
Let assume the database be I from Table 2. Here the first transaction is given as I_{1} and the second transaction is given as I_{2} and so on. The maximum length is defined as I_{max}, V_{I} is the number of transactions. The closest higher perfect square of V_{I} is referred by V_{I}^{v}. The I_{max} value as per the table is 3. Vi = 5, V_{I}^{v} = 9 (perfect square next to 5 is 9).
The Figure 2 shows the sanitized data, here I' is achieved by the sanitized key generated by the key generation process in the original database. The outcome key matrix M_{2} and I is binarized for carrying the XOR operation. Subsequently, the unit step input is summed up and I' is achieved as per the Equation (1).
$$\begin{array}{}\text{(1)}& {I}^{\prime}=({M}_{2}\oplus I)+1\end{array}$$The key generation process for doing sanitization is resembled in the Figure 3. The key is generated by the PSVGWO model by randomly initializing the population of various keys. This is followed after the sanitization process, from where the sanitized database is achieved. At the same time, the sanitization process that attains the sanitized database along with the original database attain the association rule and evaluates the objective functions as o_{1} ,o_{2}, and o_{3}, respectively. Finally, the key value is constantly updated until attaining the extreme termination measure and gains the best needed solution. The key is optimally produced by the proposed PSVGWO method for data sanitization process. The length of the chromosome is allocated on the basis of value. The parameters are defined by , here V refers to the original database. As per the Table 2, max(V) = 4 that is the database’s largest item set.
The decoding process is illustrated in Figure 4. The sanitization process offers I' and M_{2} from key generating criteria are needed to be binarized in this decoding process. The sanitized database from binarization block is minimized from unit input step. Meanwhile, the XOR operation is carried out in the key matrix that is binarized and the database after minimization, and the restored database is recovered. Further, it is expressed as; the key generation process produces sanitized key, which is exploited to do the restoration of database I. It is utilized to create the sanitized database I^{'}, from which the lossless restoration cloud takes place with respect to Equation (2). Here Î refers to the restored data and the sanitizing key matrix is defined by M_{2} that are recreated by M. The algorithm for the restoration process is given in Algorithm 1.
$$\begin{array}{}\text{(2)}& \hat{I}=({I}^{\prime}1)\oplus {M}_{2}\end{array}$$The chromosomes (key) M that are utilized for the process of sanitization are given to the proposed PSVGWO algorithm. The number of key ranges between M_{1} and M_{2} is optimized by deploying the proposed PSVGWO method; thereby the optimal key is attained. The Figure 5 illustrates the solution encoding process in which the chromosome or key length is given as .
The transformation of the chromosome M in the solution transformation process is done by Khatrirao product. At first, M is reconstructed as M_{1} with matrix dimension . For example, the reconstruction process of M = 0,2,1 takes place the rowwise duplication that generates the key matrix, the Equation (3) shows the dimension of M_{1} as , here the row matrix is allocated on the basis of and the column matrix is on the basis of I_{max}.
Rebuild Mwith size
$$\begin{array}{}\text{(3)}& {M}_{1}=\left[\begin{array}{c}0\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}0\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}0\\ 2\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}2\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}2\\ 1\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}1\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}1\end{array}\right]\end{array}$$Consequently, M_{2} with key dimension [V_{I} x I_{max}] is accomplished by Khatrirao product as Mi ⊗ Mi, here ⊗ refers to the Kronecker product and the sizes are trimmed with respect to the original data base dimension is given in Equation (4).
$$\begin{array}{}\text{(4)}& {M}_{2}=\left[\begin{array}{c}0\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}0\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}0\\ 2\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}2\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}2\\ 1\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}1\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}1\end{array}\right]\otimes \left[\begin{array}{l}0\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}0\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}0\\ 2\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}2\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}2\\ 1\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}1\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}1\end{array}\right]\end{array}$$On the basis of the Khatrirao function, the key generation is performed by the M_{1}, thereby it creates the similar matrix that is equivalent to original database M_{2} [V_{I} x I_{max}]. Finally, the rule hiding process is involved to attain a sanitization data base, I' by hiding the sensitive data. Additionally, the binarization of key matrix and the original database takes place. Because of this, the rule hiding operation is process with the binarized key matrix pruning, in which the binarized original database carries out the XOR function by similar matrix sizes and added up by the one that created the sanitized database, given in Equation (10), here M_{2} defines the pruned key matrix. Added to this, I’ withdraw the association rules and the sensitive rules that achieved from the sanitization process which is prior to the M sanitization. Hence Equation (1) is evaluated and the sanitized database I' is achieved on the basis of Khatrirao process.
Subsequent to the generation of the sensitive and association rules of sanitized and original database, the three objective functions o_{1} ,o_{2}, and o_{3} are calculated with respect to the Equations (5), (6) and (7). Here in Equation (5), Q_{K} denotes the sensitive item set frequency in sanitized information and Q_{G} denotes the sensitive item set frequency in original information. Consequently, Q_{N} in Equation (6) denotes the nonsensitive item set frequency in sanitized information. The Euclidean distance among the original information I and sanitized information I' is given in Equation (7). The distance among every item set/element in sanitized and original information is denoted by o_{4} and is shown in Equation (8). Moreover, the proposed structure’s fitness function is denoted as Q.
$$\begin{array}{}\text{(5)}& {o}_{1}=\frac{{Q}_{K}}{{Q}_{G}}\end{array}$$ $$\begin{array}{}\text{(6)}& {o}_{2}=\frac{{Q}_{N}}{{Q}_{G}}\end{array}$$ $$\begin{array}{}\text{(7)}& {o}_{3}=D(I,{I}^{\prime})\to Euclidean\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}distance\end{array}$$ $$\begin{array}{}\text{(8)}& \begin{array}{l}Q={\displaystyle \frac{{z}_{1}{o}_{1}}{max[{o}_{1,}{o}_{2}]}}+{z}_{2}[1{\displaystyle \frac{{o}_{2}}{max[{o}_{1,}{o}_{2}]}}]\\ [3ex]\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}+{z}_{3}\left[{\displaystyle \frac{{o}_{3}}{max({o}_{4})}}\right]\end{array}\end{array}$$The implemented privacy prevention’s objective function in medical data is shown in Equation (9).
$$\begin{array}{}\text{(9)}& H=Min(Q)\end{array}$$To model the social hierarchy of wolves mathematically while making GWO [22], the fittest solution is defined as alpha (α). The better solution of second and third is given as beta (β) and delta (δ), correspondingly. The remaining solution of the candidate is referred to as omega (ω). The hunting or optimization in GWO algorithm is aided with the help of α, β and δ. The wolves ω chase these wolves.
Encircling Prey: Atthe time of hunting process the grey wolves encircle the prey, and this encircling behavior is proposed mathematically by this following Equations (10) and (11), respectively.
$$\begin{array}{}\text{(10)}& \begin{array}{rcl}& \overrightarrow{B}=\overrightarrow{E}.{\overrightarrow{M}}_{q}(u)\overrightarrow{M}(u)& \end{array}\end{array}$$ $$\begin{array}{}\text{(11)}& \begin{array}{rcl}& \overrightarrow{M}(u+1)={\overrightarrow{M}}_{q}(u)\overrightarrow{H}.\overrightarrow{B}& \end{array}\end{array}$$Here, the current iteration is given by u. and are referred as the coefficient vector. The position vector of the prey is given as _{q} and the grey wolf position vector is given as M. The vector calculation of and are given below in Equations (12) and (13), respectively.
$$\begin{array}{}\text{(12)}& \begin{array}{rcl}\overline{H}& =& 2\overrightarrow{e}.{\overrightarrow{s}}_{1}\overrightarrow{e}\end{array}\end{array}$$ $$\begin{array}{}\text{(13)}& \begin{array}{rcl}\overline{E}& =& 2.{\overrightarrow{s}}_{2}\end{array}\end{array}$$Here, the component is reduced linearly between 2 to 0 for a course of iteration and and are random vectors within [0, 1].
Hunting: Grey wolves have a special capability of identifying the prey location and surround them. Generally, the alpha aided the hunt, and occasionally beta and gamma joined with them. Though within the certain search space, the location of the prey is not known. The hunting behavior of grey wolves is mathematically simulated, by using alpha, beta and delta wolves’ better knowledge on the possible location of the prey. The first three best solutions are taken into account, whether the rest is compelled. The Equations (14), (15) and (16) is given as follows.
$$\begin{array}{}\text{(14)}& \begin{array}{l}{\overrightarrow{B}}_{\alpha}={\overrightarrow{E}}_{1}.{\overrightarrow{M}}_{\alpha}\overrightarrow{M},{\overrightarrow{B}}_{\beta}={\overrightarrow{E}}_{2}.{\overrightarrow{M}}_{\beta}\overrightarrow{M},\\ {\overrightarrow{B}}_{\delta}={\overrightarrow{E}}_{3}.{\overrightarrow{M}}_{\delta}\overrightarrow{M}\end{array}\end{array}$$ $$\begin{array}{}\text{(15)}& \begin{array}{l}{\overrightarrow{M}}_{1}={\overrightarrow{M}}_{\alpha}{\overline{H}}_{1}.({\overrightarrow{B}}_{\alpha}),{\overrightarrow{M}}_{2}={\overrightarrow{M}}_{\beta}{\overline{H}}_{2}.({\overrightarrow{B}}_{\beta}),\\ {\overrightarrow{M}}_{3}={\overrightarrow{M}}_{\delta}{\overline{H}}_{3}.({\overrightarrow{B}}_{\delta})\end{array}\end{array}$$ $$\begin{array}{}\text{(16)}& \overrightarrow{M}(u+1)=\frac{{\overrightarrow{M}}_{1}+{\overrightarrow{M}}_{2}+{\overrightarrow{M}}_{3}}{3}\end{array}$$Attacking Prey: the model was mathematically implemented by reduce the value of , while approaching the prey. Hence fluctuation range also reduces with the . Further, it says that is a random value with interval [2e, e], whether component is reduced linearly between 2 to 0 for a course of iteration.
The PSO [21] model is proposed originally to stimulate the bird flock’s social behavior, yet the simplification is made in this algorithm and has understood that the individuals are termed as particles, which are performing the optimization.
In this PSO model, initially the particles are placed randomly within the search space, i.e., randomly moved in defined directions. The particle’s direction can changed gradually, and hence it started to go along the direction of the previous best position by itself. Then it searches the neighborhood and discovered the best positions regarding some fitness function. fit = S^{m} — S.
Here the particle’s position is given as and velocity be . At first, these two variables are randomly chosen, after that in accordance with two formulas it is iteratively updated and is given in Equation (17)
$$\begin{array}{}\text{(17)}& \overrightarrow{w}=\omega \overrightarrow{w}+{c}_{1}{r}_{1}(\overrightarrow{q}\overrightarrow{M})+{c}_{2}{r}_{2}(\overrightarrow{f}\overrightarrow{M})\end{array}$$Here, userdefined behavioral parameter w is referred as an inertia weight that controlled the recurrence amount in the velocity of particles. The preceding best position (personal best) of particle is and is the preceding best position in swarm (global best); thereby which the particles implicitly communicate with one another. This is weighted by using the stochastic variable r_{1} ,r_{2} ~ U(0,1) and c_{1}, c_{2} is the acceleration constant. The velocity is added to the current position of the particle to move to the next position in search space, in spite of any fitness improvement.
$$\begin{array}{}\text{(18)}& \overrightarrow{M}\leftarrow \overrightarrow{M}+\overrightarrow{w}\end{array}$$Though the conventional algorithms have better performance in optimization problems, still they pose some limitations that have to be rectified. The conventional GWO algorithm also poses some limitations such as slow convergence, bad local searching ability, and low solving precision. The PSO algorithm yet poses some drawbacks such as it still needs an improvement over the wide range of field. More work is needed further for improving the convergence and the robustness. To overcome these problems, this paper aims to introduce a new hybrid algorithm. The proposed PSVGWO is explained as follows: Here, the PSO characteristic is incorporated in GWO algorithm. In the proposed model, the encircling of prey mathematical model is given in Equations (10) and (11). The hunting process mathematical model is illustrated by the Equations (14), (15) and (16). The major modification of proposed model is goes with the position update. The new position update of the PSV GWO algorithm is given in Equation (19). Here is the velocity of the position update of PSO and it is given in Equations (17) and (18).
$$\begin{array}{}\text{(19)}& M(u+1)=\frac{{\overrightarrow{M}}_{1}+{\overrightarrow{M}}_{2}+{\overrightarrow{M}}_{3}+{\overrightarrow{M}}_{}}{4}\end{array}$$In the conventional PSO algorithm, both c_{1}, c_{2} is said to be acceleration constant. Here in the proposed algorithm, c_{1}, c_{2} is varied in accordance with the values 0.1,0.3,0.5,0.7 and 1. The PSVGWO based optimal key selection is given by the Algorithm 2, and the pseudo code for the proposed model is illustrated in Figure 6.
Algorithm 2 PSVGWO based Optimal Key Selection 
Initialize the grey wolf population Mi(i = 1, 2,n) 
Initialize e, H and E 
Estimate the fitness value for every search agent 
M_{α} = the best search agent 
M_{β} = the second best search agent 
M_{δ} = the third best search agent 
while(u < max iteration) 
for every search agent 
Update the current position of search agent by Equation (19) 
end for 
Update e, H and E 
Estimate the fitness value of entire search agent 
Update M_{α}, M_{β} and M_{δ} 
u = u + 1 
end while 
Return M_{α} 
The PSVGWO algorithm for the preservation of medical data has been implemented in JAVA. Four medical datasets are used for the simulation process that includes AutismAdolescent dataset, AutismChild dataset, Cryotherapy dataset and Immunotherapy dataset. Further, the performance of the proposed method is compared over the conventional methods like PSO [21, 26, 28], GA [23], DE [24], CSA [25] and AAPCSA [27] algorithms on the basis of recovered data. Additionally, on the basis of the different attacks, the simulation was done namely, Known Plaintext Attack (KPA), Known Cipher Attack (KCA), Chosen Plaintext Attack (CPA), and Chosen Cipher Attack (CCA). Moreover, the analysis has been made by the variation in c_{1} and c_{2} regarding the cost function, and the outcome was thus demonstrated.
The various types of attack such as KCA and KPA were analyzed and compared with the existed algorithms, and it is shown in Figure 7. The KCA attack with respect to the proposed scheme in Figure 7(a) is 0.13% better than the PSO and GA, also 0.12% better from DE and CSA. From Figure 7(b), the reduction of KPA attack over the proposed model is 0.35% and 0.01% better than PSO and AAPCSA. It is also 0.30% better than the remaining GA, DE and CSA algorithms. The CCA and CPA attacks on the four datasets are illustrated in Figure 8. In Figure 8(a), the dataset with autism adult with respect to CCA is 0.32%, and 0.30% better from PSO and GA and also 0.29% superior to DE and CSA, respectively. The CPA attack scheme is 0.31%, 0.29%, 0.28%, 0.27% and 0.05% better from PSO, GA, DE, CSAandAAPCSA, respectively. In autism child dataset with regards to the CCA scheme is 0.15% better than all other conventional methods. The CPA attack under the cryotherapy dataset is 0.63% better than PSO, 0.47% superior to GA and DE and 0.42% better from CSA algorithms. The CCA attack under Immunotherapy dataset in Figure 8(d) is 0.17%, 0.16%, 0.12% and 0.10% better from PSO, GA, DE and CSA, respectively. Hence the analysis shows that the proposed model has an improvement over the existed ones in terms of attacks.
The PSVGWO model restoration process for the four datasetis shown in Tables 3, 4, 5 and 6. In Table 3, the result of the proposed model with respect to the autism adult dataset for C_{1} is 99.10%, 99.18%, 99.46%, 95.49%, and 93.68% better than PSO, GA, DE, CSA, and AAPCSA, respectively. For C_{3}, the implanted method is 75.81% better from GA and also for F, the implemented model is 38.99% superior to AAPCSA. Table 4 shows the performance of proposed model over other methods in terms of Autism child dataset. It is observed that for C_{1}, the proposed method is 99.80%, 99.55%, 99.39%, 99.17% and 99.64% better from PSO, GA, DE, CSA, and AAPCSA, respectively. For C_{3}, the proposed method is 41.17% better than GA. From Table 5, the implemented model for autism Cryotherapy dataset in terms of C_{1} is 88.89%, 8.33%, 83.33%, 8.33% and 93.89% superior to PSO, GA, DE, CSA, and AAPCSA, respectively. For C_{3}, the introduced scheme is 2.17% better than GA. Also for F, the proposed model is 16.67%, 65.28%, and 38.62% better from PSO, DE and AAPCSE, respectively. Finally, the autism immunotherapy dataset that is illustrated in Table 6 observed that in C_{1} the recovery function is 81.81%, 9.09%, 54.54% and 69.70 superior to DA, GE, CSA, and AAPCSA. And for C_{3}, the implemented scheme is 24.88%, 0.68%, and 2.82% better from PSO, DE and AAPCSA, respectively. For F, the proposed method is 9.14%, 57.39%, and 60.62% better than PSO, DE and AAPCSA, respectively. Hence the result shown that the recovery process in the proposed model reveals an improvement over other conventional methods.
Functions  PSO [21]  GA [23]  DE [24]  CSA [25]  AAPCSA [26]  PSVGWO 
C1  5.84210526  6.444444  9.777778  1.166667  0.833333  0.052631579 
C2  0.95754499  0.954755  0.927054  0.998615  1.001385  1.008306414 
C3  427.050348  2209.028  464.5105  375.2453  488.4015  534.4541889 
F  21.1718268  2.703697  23.48654  0.612257  58.01838  35.39705858 
Functions  PSO [21]  GA [23]  DE [24]  CSA [25]  AAPCSA [26]  PSVGWO 
C1  3.34  1.47651  1.087248  0.805369  1.85906  0.006666667 
C2  0.94134358  0.988133  0.997827  1.004847  0.978606  1.024899733 
C3  1125.3675  2475.185  1405.386  1636.437  1055.604  1456.172067 
F  28.2255105  23.32239  61.46219  5.610919  51.53938  74.25923573 
Functions  PSO [21]  GA [23]  DE [24]  CSA [25]  AAPCSA [26]  PSVGWO 
C1  0.75  0.090909  0.5  0.090909  1.363636  0.083333333 
C2  1.00483871  1.016155  1.017771  1.016155  0.993538  1.017741935 
C3  6493.45831  8937.301  7920.009  6019.631  4537.144  8743.042174 
F  143.191788  16.53284  343.6985  38.07991  194.4128  119.3211976 
Functions  PSO [21]  GA [23]  DE [24]  CSA [25]  AAPCSA [26]  PSVGWO 
C1  0.09090909  0.5  0.1  0.2  0.3  0.090909091 
C2  1.0140647  1.007042  1.012676  1.011268  1.009859  1.014064698 
C3  12423.538  3930.424  9396.339  8369.21  9603.537  9332.909191 
F  144.945618  18.44217  311.7101  9.779016  337.2987  132.8116969 
The cost function value by varying the c_{1} and c_{2} in Equation (17) of PSVGWO model is illustrated in Figure 9–13 for the four datasets. Figure 9(a) represent the proposed model over the autismadult dataset with respect to c_{1} = 0.1 and c_{2} = 0.1, the obtained value of C_{1}, C_{2}, C3 and F is 0.052,1.008,534.45 and 35.39, respectively. For the autismchild database with regards to c_{1} =0.1 and c_{2} = 0.1 in Figure 9(b), the attained value of C_{1}, C_{2}, C_{3} and F is 0.006, 1.02, 1456.17 and 74.25. From Figure 9(c) the Cryotherapy dataset regarding the c_{1} =0.1 and c_{2} = 0.1, the achieved value of C_{1}, C_{2}, C_{3} and F is 0.083,1.017, 8743.04 and 119.32, respectively. For the autismimmunotherapy database with regards to c_{1} =0.1 and c_{2} = 0.1 in Figure 9(d), the accomplished value of C_{1}, C_{2}, C_{3} and F is 0.09, 1.014, 9332.90 and 13.28, respectively.
Additionally, the Figure 10(a) illustrates the implemented model under the autismadult dataset with respect toc_{1} = 0.3 and c_{2} = 0.3, the attained value of C_{1}, C_{2}, C_{3} and F is 0.052, 1.008, 400.05 and 35.12, respectively. For the autismchild database regarding c_{1} = 0.3 and c_{2} = 0.3 in Figure 10(b), the attained value of C_{1}, C_{2}, C_{3} and F is 0.006, 1.02, 1278.73 and 73.32. From Figure 10(c) the Cryotherapy dataset with regards to the c_{1} = 0.3 and c_{2} = 0.3, the obtained value of C_{1}, C_{2}, C_{3} and F is 0.083, 1.017,4973.08 and 86.96, respectively. For the immunotherapy database with regards to c_{1} = 0.3 and c_{2} = 0.3 in Figure 10(d), the gained value of C_{1}, C_{2}, C_{3} and F is 0.09, 1.014, 9654.89 and 14.83, respectively.
Similarly, the Figure 11(a) symbolize the autismadult dataset over the proposed model with respect to c_{1} = 0.5 and c_{2} = 0.5, the gained value of C_{1}, C_{2}, C_{3} and F is 0.052, 1.008, 377.62 and 31.43, respectively. Further, the autismchild database with respect to c_{1} = 0.5 and c_{2} = 0.5 in Figure 11(b), the attained value of C_{1}, C_{2}, C_{3} and F is 0.006, 1.02, 2715.42 and 91.16. From Figure 11(c) the Cryotherapy dataset regarding the c_{1} = 0.5 and c_{2} = 0.5, the obtained value of C_{1}, C_{2}, C_{3} and F is 0.083, 1.017, 5437.35 and 76.32, respectively. Moreover, for the autismimmunotherapy database with respect to c_{1} = 0.5 and c_{2} = 0.5 in Figure 11(d), the attained value of C_{1}, C_{2}, C_{3} and F is 0.09, 1.014, 1282.73 and 14.99, respectively
On considering the autismadult dataset in Figure 12(a) for c_{1} = 0.7 and c_{2} = 0.7, the obtained values of C_{1}, C_{2}, C_{3} and F are 0.105, 1.007, 456.52 and 29.26, respectively. The Child dataset over the proposed method in Figure 12(b) for c_{1} = 0.7 and c_{2} = 0.7, the gained value is 0.006, 1.024, 1396.38 and 72.57, respectively. The implemented model under the cryotherpy dataset scheme in Figure 12(c) with c_{1} = 0.7 and c_{2} = 0.7, the accomplished value of C_{1}, C_{2}, C_{3} and F is 0.08,1.017,4394.39 and 72.48, correspondingly. For the immunotherapy dataset in Figure 12(d) with respect to c_{1} = 0.7 and c_{2} = 0.7, the value that attained is 0.09,1.014,1291.94 and 14.92, respectively.
Finally, the Figure 13 explains the four dataset with respect to the CCA and CPA attacks. Here, the autismadult dataset with regard to c_{1} = 1 and c_{2} = 1, the value that gained for C_{1}, C_{2}, C_{3} and F is 0.052,1.008, 695.18 and 41.18, correspondingly. The proposed model under the child dataset for the optimization function c_{1} = 1 and c_{2} = 1 is analyzed, the achieved value of C_{1}, C_{2} ,C_{3} and F is 0.293,1.017,1720.37 and 77.12, respectively. The cryotherpy dataset regarding the c_{1} = 1 and c_{2} = 1, the attained value is 0.083, 1.017, 1299.17 and 13.51. The immunotherapy over the proposed model is analyzed with c_{1} = 1 and c_{2} = 1, and the result that obtained is 0.09,1.014,1902.36 and 15.76 for C_{1}, C_{2}, C_{3} and F, respectively. Thus the implemented method was effectively solved over various attacks by varying the optimization function c_{1} and c_{2}.
In this paper, the privacy preservation method in medical data was implemented. The main focus of this recommended technique was to introduce an effective sanitization process to hide the client’s sensitive rules. A key was created for hiding the private healthcare data, which can be chosen optimally with the help of the PSVGWO algorithm. Further, the resultant sanitized data was recovered securely by the authorized user. Additionally, the result was obtained by comparing the proposed model over traditional algorithms. From the experimental analysis, it is shown that the proposed scheme was evaluated with regards to the various attacks and the result were attained. The analysis over the attacks on the proposed model under the cryotherapy dataset is 0.63% better than PSO, 0.47% superior to GA and DE and 0.42% better from CSA algorithms. The CCA attack under Immunotherapy dataset is 0.17%, 0.16%, 0.12% and 0.10% better from PSO, GA, DE and CSA, respectively. The dataset with autism adult with respect to CCA is 0.32%, and 0.30% better from PSO and GA and also 0.29% superior to DE and CSA, respectively. Thus, the simulation result shows that the implemented method has an efficient performance over the traditional algorithms.
[1] Li, Y., Bai, C and Chandan Reddy, K. (2016). A distributed ensemble approach for mining healthcare data under privacy constraints, Information Sciences, 330, 245–259.
[2] Ni, L., Li, C., Wang, X., Jiang, H and Yu, J. (2018). DPMCDBSCAN: Differential Privacy Preserving MultiCore DBSCAN Clustering for Network User Data, IEEE Access, 6, 21053–21063,
[3] Gao, G., Wan, X., Yao, S., Cui, Z., Zhou C and Sun, X. (2017). Reversible data hiding with contrast enhancement and tamper localization for medical images, Information Sciences, 385–386, 250–265.
[4] Zhang, L., Liu, Y., Wang, R., Fu, X and Lin, Q. (2017). Efficient privacypreserving classification construction model with differential privacy technology, Journal of Systems Engineering and Electronics, 28(1), 170–178.
[5] Kim, S., Lee, H and DohnChung, Y. (2017). Privacypreserving data cube for electronic medical records:An experimental evaluation, International Journal of Medical Informatics, 97, 33–42.
[6] Fong, P. K and WeberJahnke, J. H. (2012). Privacy Preserving Decision Tree Learning Using Unrealized Data Sets, IEEE Transactions on Knowledge and Data Engineering, 24(2), 353–364.
[7] Poulis, G., Loukides, G., Skiadopoulos, S and GkoulalasDivanis, A. (2017). Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints, Journal of Biomedical Informatics, 65, 76–96.
[8] Xu, L., Jiang, C., Chen, Y., Wang, J and Ren, Y. (2016). A Framework for Categorizing and Applying PrivacyPreservation Techniques in Big Data Mining, Computer, 49(2), 54–62.
[9] Bhaduri, K., Stefanski, M. D. and Srivastava, A. N. (2011). PrivacyPreserving Outlier Detection Through Random Nonlinear Data Distortion, IEEE Transactions on Systems, Man, and Cybernetics, PartB (Cybernetics), 41(1), 260–272.
[10] Zhang, N and Zhao, W. (2007). PrivacyPreserving Data Mining Systems, Computer, 40(4), 52–58.
[11] Wang, J., Deng, C and Li, X. (2018). Two PrivacyPreserving Approaches for Publishing Transactional Data Streams, IEEE Access, 6, 2364823658.
[12] Terrovitis, M., Poulis, G., Mamoulis, N and Skiadopoulos, S. (2017). Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories, IEEE Transactions on Knowledge and Data Engineering, 29(7),1466–1479.
[13] Ahluwalia, M. V., Gangopadhyay, A., Chen, Z and Yesha, Y. (2017). TargetBased, Privacy Preserving, and Incremental Association Rule Mining, IEEE Transactions on Services Computing, 10(4), 633–645.
[14] Lin, K. P and Chen, M. S. (2011). On the Design and Analysis of the PrivacyPreserving SVM Classifier, IEEE Transactions on Knowledge and Data Engineering, 23(11), 1704–1717.
[15] Fung, B. C. M., Wang, K. and Yu, P. S. (2007). Anonymizing Classification Data for Privacy Preservation, IEEE Transactions on Knowledge and Data Engineering, 19(5), 711–725.
[16] Upadhyay, S., Sharma, C., Sharma, P., Bharadwaj, P., Seeja, K. R. (2016). Privacy preserving data mining with 3D rotation transformation, Journal of King Saud University – Computer and Information Sciences, Available online 28 November 2016.
[17] BALOGLU, U. B., DEMIR, Y. (2018). Lightweight PrivacyPreserving Data Aggregation Scheme for Smart Grid Metering Infrastructure Protection, International Journal of Critical Infrastructure Protection, Available online 10 May 2018.
[18] Lin, C. (2016). A reversible data transform algorithm using integer transform for privacypreserving data mining, Journal of Systems and Software, 117, 104–112.
[19] Dhasarathan, C., Thirumal, V., Ponnurangam, D. (2017). A secure data privacy preservation for ondemand cloud service, Journal of King Saud University – Engineering Sciences, 29(2), 144–150.
[20] Aldeen,Y.A.A. S., Salleh, M.,Aljeroud,Y. (2016). An innovative privacy preserving technique for incremental datasets on cloud computing, Journal of Biomedical Informatics, 62, 107–116.
[21] Marini, F., Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial, Chemometrics and Intelligent Laboratory Systems, 149, pp. 153–165.
[22] Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey Wolf Optimizer, Advances in Engineering Software, 69, 46–61.
[23] McCal, J. (2005). Genetic algorithms for modelling and optimisation, Journal of Computational and Applied Mathematics, 184(1), 205–222.
[24] Zheng, L. M., Zhang, S. X., Tang, K.S., and Zheng, S. Y. (2017). Differential evolution powered by collective information, Information Sciences, 399, 13–29.
[25] Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Computers & Structures, 169, 1–12.
[26] Sridhar Mandapati, Dr. Raveendra Babu Bhogapathi and Dr. M. V. P. Chandra Sekhara Rao, (2013). Swarm Optimization Algorithm for Privacy Preserving in Data Mining, IJCSI International Journal of Computer Science Issues, Vol. 10(2), 1694–0784.
[27] Mandala, J., Dr. M. V. P. Chandra Sekhara Rao, (2018). Privacy Preservation of Data Using Crow Search with Adaptive Awareness Probability, in communication.
[28] G. Kalyani, Dr. M. V. P. Chandra Sekhara Rao, (2017). Particle Swarm Intelligence and Impact FactorBased Privacy Preserving Association Rule Mining for Balancing Data Utility and Knowledge Privacy, Arabian Journal for Science and Engineering, 43(8), 4161–4178.
[29] De Giorgio A., Loscalzo R. M., Ponte M., Padovan A. M., Graceffa G. and Gulotta F. (2016). An innovative mindfulness and educational care approach in an adult patient affected by gastroesophageal reflux: the IARA model, 14(4).
[30] Satish Ramchandra Todmal, Suhas Haribhau Patil. (2015), Optimal Image Watermarking using Hybrid Optimization Algorithm, International Journal of Computer Vision and Image Processing (IJCVIP), 5(1), 27–47.
Jyothi Mandala received her B.Tech degree in Computer Science and Information Technology from AITAM College, Tekkali, India and M.Tech degree in Computer Science and Engineering from JNTUK, Kakinada, India. Present she is pursuing her Ph.D. in the area of Privacy Preserving Data. She has 12 years of teaching experience. Currently she is working as Assistant professor in Information Technology Department at GMRIT, Rajam. Her areas of interest are Information Security and Data Mining.
Dr. M. V. P. Chandra Sekhara Rao received his M.Tech degree in Computer Science and Engineering from JNTUK, Kakinada, India and Ph.D from JNTUH, Hyderabad in 2012. He has nearly 22 years of teaching experience. Currently he is working as professor in Computer Science Engineering Department at RVR & JC College of Engineering, Guntur. His areas of interest are Data Warehousing and Data Mining.
Journal of Cyber Security and Mobility, Vol. 8_4, 439–466.
doi: 10.13052/jcsm22451439.843
This is an Open Access publication. © 2019 the Author(s). All rights reserved.
3 Modeling of Medical Data Privacy Preservation
4 Optimal Key Extraction Strategy: Proposed Sanitization and Restoration Model