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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 Balancing Security overhead and Performance Metrics using a novel Multi-Objective Genetic Approach Wasim Khalil Shalish 1, A. Z. Ghalwash2 , H. M. El-Deeb3 and K. Badran4 1 2 Syrian Armed Forces Helwan University 3 4 Modern University Military Technical College Abstract: With fast progress of the networks, data mining solve privacy problem, Privacy-Preserving Data Mining and information sharing techniques, the security of the (PPDM) has become a hotspot in data mining and privacy of sensitive information in a database becomes a vital database security fields. issue to be resolved. The mission of association rule mining is Recent advances in data mining algorithms increased the discovering hidden relationships between items in database risk of information leakage and its confidence issue. and revealing frequent item sets and strong association rules. Because of this progress, the parallel research area has Some rules or frequent item sets called sensitive which been started to overcome the information leakage risks contain some critical information that is vital or private for its owner. and immunization of mining environment. Privacy In recent research there is GA, users have tried to combine preserving against mining algorithms is a new research (or aggregate) multiple objectives into a single scalar area that investigates the side-effects of data mining function using different weights for each objective, or by methods that is derived from the privacy diffusion of adding penalty functions for specific objectives. But these persons and organizations. Mining these effects can be methods add more adjustable parameters which require considered as an optimization problem. profound domain knowledge which is usually not available. In addition, the solutions generated are usually very sensitive Optimization Technique to small changes in these weights or penalties functions. In Optimization techniques are used for optimizing this paper, we propose a method that solves those constraints problems in which one needs to minimize or maximize a by using multi objective fitness functions, where it leaves the real function by methodically choosing the values of real choice for user to minimize or maximize many objectives or integer variables from within a particular set. It is depending on his or her problem. We investigate the problem using Multi-Objective Genetic Algorithm to find optimum finding the "best available" values of some objective state of modification. Finally we establish some experiments function given a defined area, including a variety of and test our approach by datasets. The experimental results different types of objective functions and different types of showed that the number of sensitive rules in sanitized data set domains. Many types of optimization techniques and (hiding failure) equal to zero. The number of non- sensitive optimization algorithms are used in various types of patterns discovered from the original database D and the approaches. In this paper we use the genetic algorithm for sanitized database is different. Since we hide most of the minimizing the cost function. patterns considered sensitive from the original data set, thus the miss cost (MC) is equal to 36%. The percentage of the Genetic Algorithm discovered patterns that are artifacts (AP) is 27%. The The genetic algorithm (GA) is an optimization and search percentage of the dissimilarity (DISS) between the original technique based on the ethics of genetics and usual and the sanitized datasets is 26%. The amount of non- sensitive association rules that are removed as an effect of the selection. GA allows a population composed of many sanitization process is four. individuals to develop under particular selection rules to a state that maximizes the “fitness” (i.e., minimizes the cost Keywords: MOPP, MOGA, DBMS, MST, MCT and function). GA. In GA, a population consists of a cluster of individuals called chromosomes that signify a complete solution to a certain problem. Each chromosome is a sequence of 0s or 1-INTRODUCTION 1s. The initial set of the population is an erratically Nowadays, due to successful applications of data mining generated set of individuals. A new population is techniques, they have been demonstrated in many areas generated by two methods: steady state Genetic algorithm that benefit commercial, social and human activities. and generational Genetic Algorithm. The steady-state Along with the success of these techniques, they pose a Genetic Algorithm replaces one or two members of the threat to privacy. One can easily disclose other’s sensitive population; whereas the generational Genetic Algorithm information or knowledge by using these techniques. So, replaces all of them at each generation of progression. In before releasing database, sensitive information or this work a steady-state Genetic Algorithm is adopted as knowledge must be hidden from unauthorized access. To Volume 2, Issue 3 May – June 2013 Page 407 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 population replacement method. This method tries to process, frequent items are updated through crossover keep a certain number of the best individuals from each operation. Crossover is the main process of genetic generation and copies them to the new generation. algorithm so in this step most of the frequent items Each transaction is represented as a chromosome and become infrequent. Remaining items are modified in the occurrence of an ith item in transaction showed by 1 and mutation process. After ensuring the conditions i.e. all the non occurrence of the item by 0 in ith bit of transaction. sensitive items are modified then the process is completed The fitness of a chromosome is dogged by several and the execution is terminated. Finally, a priori methods and different strategies. Each population consists algorithm has been applied to the modified database for of several chromosomes and the best chromosome is used finding the frequent item sets for generating the sensitive to generate the next population. For the initial population, rules. Now, we have to ensure that all the sensitive rules a large number of random transactions are preferred. are hidden; no false rules are generated from the dataset Based on the survival fitness, the population will make and the non sensitive items are not affected. over into the future generation. The rest of paper is organized as follows: Section 2 gives a summary of the high-tech methodologies and related Fitness function works for privacy preserving in data mining and Fitness function is defined over the genetic representation association rule hiding with dataset sanitization. Section and measures the superiority of the represented solution. 3 describes problem formulation and enlightens the major The fitness function is forever problem dependent. Once concepts upon which we base the proposal for the new we have the genetic representation and the fitness privacy preserving framework. Section 4 introduces our function defined, GA proceeds to initialize a population proposed solution for dataset sanitization against of solutions randomly, and then improves it through association rule mining. Section 5 presents the repetitive application of mutation, crossover, and experiments we performed in large scale datasets to inversion and selection operators. introduce our approach and to prove the effectiveness of our method. Finally the conclusion will be given in Selection section 6. In selection process, the individuals producing offspring are elected. The selection step is preceded by the fitness assignment which is based on the objective value. This 2- RELATED WORK fitness is used for the real selection process. Researchers have proposed several approaches for knowledge hiding, in context of association rule hiding. Crossover Chirag et al. in [2] introduced two heuristic blocking Main function of crossover operation in genetic based algorithms named ISARC (Increase Support of algorithms is to blend two chromosomes mutually to Common Antecedent of Rule Clusters) and DSCRC generating novel offspring (child) [1]. Crossover occurs (Decrease Support of Common Consequent of Rule only with some probability (crossover probability). Clusters) to preserve privacy for sensitive association Chromosomes are not subjected to crossover remain rules. Proposed algorithms cluster the sensitive rules unmodified. The perception following crossover is the based on some criteria and hide them in fewer selected exploration of new solutions and abuse of old solutions. transactions by using unknowns (“?”). They preserve Better fitness chromosomes have a prospect to be selected certain privacy for sensitive rules in database, while more than the inferior ones, so good solution always alive maintaining knowledge discovery. to the next generation. There are different crossover A new multi-objective method was introduced for hiding operators that have been developed for various purposes. sensitive association rules based on the concept of genetic Single point crossover and multi-point are the most algorithms in [3]. The main purpose of this method is famous operators. In this paper single-point crossover has fully supporting security of database and keeping the been applied to make a new offspring. utility and certainty of mined rules at highest level. In their work, they have used four sanitization strategies Mutation such as confidence, support, hybrid and max-min. They Mutation is a genetic operator that alters one or more introduced the idea of both rule and item set sanitization, gene values in a chromosome from its initial state. This which complements the old idea behind data sanitization. can result in entirely new gene values being added to the In [4], two algorithms were proposed ISL (Increase gene pool. With these new gene values, the genetic Support of LHS) and DSR (Decrease Support of RHS). algorithm may be able to arrive at better solution than Predicting items are given as input for both algorithms to was previously possible. automatically hide sensitive association rules without pre- First the sensitive items and number of modifications mining and selection of hidden rules. required for each sensitive item are initialized. Next In [5], two algorithms, DCIS (Decrease Confidence by fitness function is evaluated for each transaction. Based Increase Support) and DCDS (Decrease Confidence by on this fitness values, each transaction selection process Decrease Support) were proposed to automatically hide are carried out in the third step. After the selection Volume 2, Issue 3 May – June 2013 Page 408 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 collaborative recommendation association rules without Where fD(i), fD’ (i) represent the frequency of the ith item in pre-mining and selection of hidden rules. The ISL and the dataset D and D’ respectively, and n is the number of DCIS algorithms have tried to increase the support of left distinct items in the original dataset D. hand side of the rule. Furthermore, DSR and DCDS Miss Cost (MC) quantifies the percentage of the algorithms have tried to decrease the support of the right nonrestrictive patterns that are hidden as side-effects of hand side of the rule. It is observed that ISL requires the sanitization process. It is computed as follows: more running time than DSR. Also both algorithms R P ( D) R P ( D) exhibit contrasting side effects. DSR algorithm shows no MC (2) hiding failure (0%), few new rules (5%) and some lost R P ( D) rules (11%). ISL algorithm shows some hiding failure Where R P (D) is the set of all non sensitive rules in the (12.9%), many new rules (33%) and no lost rule (0%). Algorithm DCIS requires more running time than DCDS. original database D and R P ( D ) is the set of all non DCIS and DCDS also exhibit contrasting side effects sensitive rules in the sanitized data base D . As one can similar to ISL and DSR algorithms. DCDS algorithm notice that there exists a compromise between the miss shows no hiding failure (0%), few new rules (1%) and cost and the hiding failure, since the more sensitive some lost rules (4%). DCIS algorithm shows no hiding association rules need to hide, the more legitimate failure (0%), many new rules (75%) and no lost rule association rules are expected to miss. (0%). Similar to the measure of miss cost, Side-Effect Factor In [6], an algorithm DSC (Decrease Support and (SEF) is used to quantify the amount of non-sensitive Confidence) was proposed in which pattern-inversion tree association rules that are removed as an effect of the was used to store related information so that only one sanitization process. It is defined as follows: scan of database is required. The proposed algorithm can P ( P Rp( D) ) SEF (3) automatically sanitize informative rule sets without pre- P Rp( D) mining and selection of a class of rules under one Artificial patterns (AF) quantify the percentage of the database scan. There are about 4% of new rules generated discovered patterns that are artifacts. It is computed as and about 9% of rules are lost on the DSC algorithm and follows: it also shows hiding failure for two predicting items. Border based approach was presented in [7-9]. It hides P P P AP (4) sensitive association rule by modifying the borders in the P lattice of the frequent and the infrequent itemsets of the Where P is the set of association rules discovered in the original database. The itemsets which are at the position original dataset D and P is the set of association rules of the borderline separating the frequent and infrequent discovered in D . itemsets forms the borders. Hiding Failure (HF) quantifies the percentage of the In [10, 11], Exact approach was provided. This approach sensitive patterns that remain exposed in the sanitized contains non heuristic algorithms which formulates the dataset. It is defined as the fraction of the restrictive hiding process as a constraints satisfaction problem or an association rules that appear in the sanitized database optimization problem which is solved by integer divided by the ones that appeared in the original dataset, programming. These algorithms can provide optimal formally: hiding solution with ideally no side effects. The related works previously described, use different HF R P ( D) (5) performance metrics; most of them use the (hiding R P (D) failure, dissimilarity, and miss cost, artificial false, and where Rp( D ) corresponds to the sensitive rules side effect). discovered in the sanitized dataset D , RP (D) to the sensitive rules appearing in the original dataset D. Performance evaluation measures for the association Ideally, the hiding failure should be 0%. The performance rules metrics for privacy preserving association rule mining The efficiency of the association rule mechanisms can be algorithms are given in [12]. characterized by the following measures: Dissimilarity quantifies the difference between the original and the sanitized datasets by comparing their histograms, where the horizontal axis contains the items 3. PROBLEM FORMULATION in the dataset and the vertical axis corresponds to their A sample transaction database D taken from [13] is frequencies. It is calculated as follows: shown in Table 1. TID shows unique transaction number. 1 n Binary valued item shows whether an item is present or Diss(D,D) n [ f D(i) f D(i) ] (1) absent in that transaction. Suppose MST and MCT are i1 fD(i) i1 selected to be 50%, 70% respectively. Table 2 shows sensitive rules satisfying MST, generated from sample database D. Volume 2, Issue 3 May – June 2013 Page 409 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 So, the possible number of association rules satisfying transactions and calculate the support and confidence of MST and MCT, generated by Apriori algorithm [14] are the candidate rules to determine if they are considerable given: , , , . Suppose the or not. A rule is considerable, if its support and rules and are specified as sensitive and confidence is higher than the user specified minimum should be hidden in sanitized database. support and minimum confidence threshold. In this way, The problem of privacy preserving in association rule algorithms do not retrieve all possible association rules mining (so called association rule hiding) that is focused that can be derivable from a dataset, but only a small by this paper can be formulated as follows: subset that satisfies the minimum support and minimum Given a transaction database (D), minimum support confidence requirements set by the users. threshold (MST), minimum confidence threshold (MCT), Apriori association rule-mining algorithm works as a set of significant association rules R mined from (D) follows. It finds all the sets of rules that appear frequently and a set of sensitive rules to be hide. enough to be considered relevant and then it derives from Generate a new database D . them the association rules that are strong enough to be Such that the rules in can be considered interesting. The major goal here is to mined from D under the same “MST” and “MCT”. preventing some of these rules that we refer to as Where no normal rules in are falsely hidden "sensitive rules", from being revealed. We want to hide (lost rules), and no extra spurious rules (ghost rules) are association rules using the best way by multi objective mistakenly will mined after the rule hiding process. genetic algorithm. Also we are interested in investigating the performance of association rules (hiding failure (HF), Table 1: Sample database D dissimilarity (DIS), artificial pattern (AF), side effect TID Item Item (Binary From) (SEF), and miss cost (MC)). 0 013 1101 Figure (1) presents the basic architecture of a database 1 1 0100 system with the association rule mechanism. 2 023 1011 3 01 1100 4 013 1101 Table 2: Sensitive rules R1 R2 Figure 1 Architecture of a database application with the 4. PROPOSED SOLUTION association rule procedure 4.1 Security and Association rule Mining Trade 4.2 Security and Association Rule Mining Trade using Optimization The association rule hiding problem can be considered as a deviation of the well identified database inference In this paper we are studying the privacy breaches which control problem in statistical and multilevel databases. incurred from certain type association rules. In doing so The primary goal in database inference control is to guard we suppose that a certain subset of association rule, which access to sensitive information that can be obtained is extracted from specific datasets, is considered as through non sensitive data and inference rules. In sensitive/critical rules. Our major goal then is association rule hiding, we think about that it is not the modification of original data source in such a way that it data itself but somewhat the sensitive association rules would be impossible for the adversary to mine the that produce a breach to privacy. sensitive rules from the modified data set as long as all For the simplicity of presentation and without loss of the remaining non sensitive information and/or knowledge remains as close as possible to this of the generality, we make the following assumptions in this original set, as our minor goal. implementation: The method developed in this paper uses binary We want to extract all association rules which satisfy transactional dataset as an input and modifies the original minimum support transaction (MST), minimum dataset based on the concept of genetic algorithms for confidence transaction (MCT). Support is a measure of privacy preserving of association rule to find the best the frequency of a rule. The confidence is a measure of solution for sanitizing original dataset based on multi- the strength of the relation between sets of rules. objective optimization. In such a way that all of sensitive Association rule mining algorithms scan the database of rules become hide and minimum modification performed Volume 2, Issue 3 May – June 2013 Page 410 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 in original dataset. The most famous possible style for To emphasis such activities in mathematical concepts, the transaction modification is distortion of original database mathematical formulation of multi-objective optimization (i.e., by replacing 1’s by 0’s and vice versa). We select problem could be defined as: this style of modification in our method. Modification of Find the vector y [ y1, y2 ,..., yn ]T which satisfies the the dataset causes so many side-effect problems. The modification process can affect the original set of m inequality constraints and the p equality constraints: rules, that can be mined from the original database, either gi ( x ) 0 i = 1,2,…,m (6) by hiding rules which are not sensitive (lost rules), or by introducing rules in the mining of the modified database, hi ( x ) 0 i = 1,2,…,p (7) which were not supported by the original database (ghost rules). We have tried to minimize these unpleasant results And optimizes (here we assume minimization) the vector by minimum and suitable modification of original dataset. function: The steps our work are explained in Figure 2. f ( x ) [ f1( x ), f 2 ( x ),..., f k ( x )]T (8) Where x [ x1, x2 ,..., xn ]T is the vector of decision variables, and the constraints given by equations (6) and (7) define the feasible region F.? Traditional Technique Convert the multi-objective optimization problem into one objective problem i.e. to find one optimal solution by combining the objectives through weighting. F w1 f ( x1) w2 f ( x 2 ) ..., wn f ( xn ) Where w 1 w2 ... wn 1 Figure 2 Multi objectives privacy preserving (MOPP) Proposed technique The following steps illustrate the methodology of the Keep the problem AS multi-objective optimization proposed solution: problem i.e. to find the pareto optimal solution Step 1: Consider a transactional database with a set of We say that a vector of decision variables y F is items and transactions. Step 2: Write two external files one for original data set optimal if there is no other x F such that and one for sensitive rules. fi ( x ) fi ( y ) for all i = 1, . . . , k and f j ( x ) f j ( y ) Step 3: Convert every chromosome to double value and for at least one j. store in population then convert that value to binary A vector u (u1 , u2 ,...uk ) is said to dominate value. Step 4: Create file for Apriori algorithm. v (v1, v2 ,..vk ) (denoted by) u v if and only if u is Step 5: Apriori algorithm is used to find the frequent item partially less than v , i.e. sets based on the minimum support threshold. Step 6: From the frequent item sets, the set of association i {1,2,..., k}, ui vi i {1,2...., k} : ui vi . rules can be generated based on the minimum support Our fitness vector consists from two elements: and confidence thresholds. R Step 7: Select the sensitive rules from the set of f1 =Hiding Failure = Sen( D) (9) RSen( D ) association rules. n Step 8: Read association rules from output file and put in 1 f 2 =Dissimilarity= [ f D(i) f D(i) ] (10) structure for comparison with sensitive rules. n Step 9: Compare association rules with sensitive to i1 f D(i) i1 calculate Fitness Vector (1). Where f D (i ) , f D (i ) represents the frequency of the ith Step 10: Compare chromosome with original dataset to item in the dataset D, and D respectively, and n is the calculate Fitness Vector (2). number of distinct items in the original dataset D. Step 11: Genetic algorithm is used for modifying the Farther, we can choose menu of optimal solutions items based on the fitness function. according to our problem. Step 12: Repeat the steps 5, 6 and 7 for the modified data The main contributions are focused on three points: first, set. a new proposed algorithm for hiding sensitive association Step 13: Verify (i) all the sensitive rules are hidden, (ii) rules using multi objective genetic algorithm and no non-sensitive rules are hidden (iii) no false rules. Modification old Math Model, the second contribution is achieving balance between security and performance in Volume 2, Issue 3 May – June 2013 Page 411 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 database, the last point of the contribution is evaluation of (hiding failure, dissimilarity, artificial pattern, side effect, hiding performance in our work. miss cost). 5. DISCUSSION AND EXPERIMENTAL Table 4: Best rules inference extracted from original RESULTS dataset with MCT=0.58 and MST=0.25 TID Rules 1 adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 5.1 Experimental Setup Class Name=democrat The data set Congress Voting Data set [15] includes votes 2 adoption-of-the-budget-resolution=y physician-fee-freeze=n aid- to-nicaraguan-contras=y 198 Class Name=democrat for each of the U.S. House of Representatives 3 physician-fee-freeze=n aid-to-nicaraguan-contras=y 211 Congressmen on the 16 key votes identified by the CQA. Class Name=democrat 210 The CQA lists nine different types of votes: voted for, 4 physician-fee-freeze=n education-spending=n 202 Class paired for, and announced for (these three simplified to Name=democrat 201 5 physician-fee-freeze=n 247 Class Name=democrat 245 yea), voted against, paired against, and announced 6 Class Name=democrat el-salvador-aid=n 200 aid-to- against (these three simplified to nay), voted present, nicaraguan-contras=y 197 voted present to avoid conflict of interest, and did not vote 7 el-salvador-aid=n 208 aid-to-nicaraguan-contras=y 204 or otherwise make a position known (these three 8 el-salvador-aid=y 212 religious-groups-in-schools=y 197 simplified to an unknown disposition). Number of Instances: 435 (267 democrats, 168 republicans) Number Table 5: shows the association rule evaluation of Attributes: 16 + class name = 17 (all Boolean valued). performance results A sample transaction database D taken from [15] is Parameters Results shown in Table (3). TID shows unique transaction HF 0% number, Suppose MST and MCT are selected 25% and MC 36% 58% respectively. AP 27% DISS 26% Table 3: Sample data set SEF 4 handicapped project-cost- adoption-of- el-salvador- Class Name the-budget- groups-in- physician- resolution fee-freeze religious- -infants sharing schools water- As shown in Table (5), and figure(3.a) the number of aid TID sensitive rules in sanitized data set equal to zero, most of the developed privacy preserving algorithms are designed 1 republican N Y N y Y Y with the goal of obtaining zero hiding failure. Thus, we 2 republican N Y N y Y Y hide all the patterns considered sensitive from the 3 democrat ? Y Y ? Y Y original data set. The number of non- sensitive patterns 4 democrat N Y Y n ? Y discovered from the original database D, and the sanitized 5 democrat Y Y Y n Y Y 6 democrat N Y Y n Y Y database is the different, since we hide most the patterns 7 democrat N Y N y Y Y considered sensitive from the original data set, thus the 8 republican N Y N y Y Y MC is equal to 36% as obviously in figure (3.b). The 9 republican N Y N y Y Y Y Y Y n N N percentage of the discovered patterns that are artifacts 10 democrat (AP) is 27% as obviously in figure (3.c). The percentage of the dissimilarity (DISS) between the original and the 5.2 Association Rules Mining Methodology using sanitized datasets is 26% as obviously in figure (3.d). The optimization amount of non-sensitive association rules that are Table (4) shows frequent rules satisfying MST, generated removed as an effect of the sanitization process is four as from sample database D, in following; the possible obviously in figure (3.e). number of association rules satisfying MST and MCT, generated by Apriori algorithm are given: (20). Suppose the rule: (el-Salvador-aid=y 212 religious-groups-in- schools=y 197) are specified as sensitive and should be hidden in sanitized database, the transactions which contain the sensitive items are called population. The chromosomes of this population the fitness function has applied. After applying the crossover and mutation operations, based on fitness function the sensitive items of the original database are modified and for keeping the privacy of the database. After modification, Apriori algorithm has been applied to verify all the sensitive rules are hidden with the same support and confidence. Then we evaluated the performance and security metrics Volume 2, Issue 3 May – June 2013 Page 412 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 3, May – June 2013 ISSN 2278-6856 6. Conclusion [11] A. Divanis, V. Verykios, “Exact Knowledge Hiding The drawbacks of the traditional techniques in [2, 3, 4, 5 through Database Extension”, IEEE Transactions on and 6] are weights values are unknown so it’s assumed in Knowledge and Data Engineering, vol. 21(5), pp. advance. Also, it is no warranty to achieve hiding failure 699–713, May 2009. with high performance. But these methods add more [12] C. Aggarwal, P.Yu, “Privacy-Preserving Data adjustable parameters which require profound domain Mining: Models and Algorithms”, Springer, knowledge which is usually not available, In addition, the Heidelberg, pp. 267–286, 2008. solutions generated in [2,3,4,5 and 6] are usually very [13] K. Duraiswamy, D. Manjula, “Advanced Approach sensitive to small changes in these weights or penalties in Sensitive Rule Hiding”, Modern Applied Science, functions. Vol.3, no. 2, 2009. The proposed approach penetrate the problem of [14] C. Clifton, M. Kantarcioglu, J. Vaidya, “Defining Balancing Security and Performance Metrics in generic Privacy for Data Mining”, In Proceedings US Nat'l way since we do optimize between hiding failure as Science Foundation Workshop on Next Generation security over head and ((AF), (Diss), (SEF), (MC)) as Data Mining, pp. 126-133, 2002. database performance metrics. [15] J. 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Verykios, “An Integer Programming Approach for Frequent Itemset Hiding”, In Proc ACM Conf Information and Knowledge Management (CIKM ’06), Nov. 2006. Volume 2, Issue 3 May – June 2013 Page 413