# A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining
## 2 Literature Review
### Privacy-Preserving Data Mining  
During the past two decades, data mining has been an efficient way to find potential and useful information from a very large database, particularly the relationships between those products that may not be easily discovered and visualized. Because information can be revealed from a database, confidential/secure information can also be discovered during the mining procedure, thereby causing privacy and security threats to users. Privacy-preserving data mining (PPDM) has become a critical issue in recent years because, not only can it find useful information, but confidential/secure data can also be hidden using a sanitization procedure; such confidential information can thus be hidden and not discovered after data sanitization. [PERSON] and [PERSON] [38] developed a novel reconstruction approach that can accurately estimate the distribution of original data. The classifiers can also be constructed to compare the accuracy between the original and sanitized data. [PERSON] et al. [10] developed hierarchical classification techniques used in PPDM. [PERSON] et al. [16] then designed an approach based on a Hamming-distance mechanism to decrease the support or confidence of sensitive information (i.e., association rules) for sanitization. [PERSON] and [PERSON] [9] provided several sanitization methods that can hide frequent itemsets through a heuristic framework. The developed algorithms use an item-restriction approach to avoid a noise addition and limit the removal of real datasets. [PERSON] and [PERSON] [39] presented a framework using the noise addition method to protect and hide individual privacy while still maintaining a high data quality. [PERSON] et al. provided an SIF-IDF method [15] to assign a weight to each transaction, and a sanitization process is then conducted from the transaction with the highest score to that of the lowest score for sanitization purposes.  
The PPDM is considered as an NP-hard problem [7; 10], however; it is thus better to provide meta-heuristic approaches to find the optimal solutions. [PERSON] and [PERSON] developed a secure protocol [40] to find a better set of rules without disclosing their own private data based on GAs [26; 27], and the result of the true positive rate times the true negative rate is then calculated to evaluate each decision rule. [PERSON] et al. then presented several GA-based approaches, including sGA2 DT, pGA2 DT [20], and cpGA2 DT [19] to hide the sensitive information by removing the victim transactions. The encoded chromosome is considered a set of solutions, and the transaction of a gene within a chromosome is regarded as the victim for later deletion. A fitness function was also developed to consider three side effects for an evaluation with pre-defined weights to show the goodness of the chromosome. Although the above algorithms are efficient at finding the optimal transactions for deletion, they still require pre-defined weights of the side effects; such a mechanism can seriously affect the final results of the designed approaches. [PERSON] et al. [21] thus developed an Evolutionary Multiobjective Optimization-Rule Hiding (EMO-RH) algorithm to hide the sensitive itemsets by removing the itemsets based on the EMO. This approach is based on a multi-objective method, although incomplete transactions can therefore be produced; a misleading decision can thus occur, particularly in the treatment of hospital diagnoses. [PERSON] et al. presented a meat-heuristic approach [23] based on the NSGA-II framework for data sanitization, which shows better side effects compared to single-objective algorithms, which is the state-of-the-art multiple-objective optimization algorithm in PPDM. Several works of privacy-preserving and security issues are extensively studied in different applications. For example, [PERSON] et al. [41] presented a new method for handling the privacy issue of independent published data. [PERSON] and [PERSON] [42] mentioned a clustering-based method for anonymity problem in IoT devices.