Inference detection in statistical database using frequent pattern

Aravind, K. N.; Anand, Amrit; Sarath, Greeshma

doi:10.1109/iccsp.2017.8286741

Cited by 2 publications

(3 citation statements)

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“…The problem of inference in data has attracted the attention of several researchers for decades, who have proposed various solutions to address this problem. Several studies have been conducted on the detection of inference attack that do not require automated learning algorithms [4,[17][18][19][20][21][22][23][24]. Each approach addresses a specific need and concerns different types of inference attacks.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, we propose a global security policy that ensures the security of data during combinations of several queries which could lead to security violations. Another example is presented in the reference [4] which proposes limiting inferences in statistical databases. In a context in which the user is only allowed to perform queries on aggregated data, this study implements a solution to prevent a combination of multiple queries from disclosing the sensitive information of a single individual.…”

Section: Related Workmentioning

confidence: 99%

“…However, these access policies are insufficient for ensuring flawless security. Indeed, the Online Analytical Processing (OLAP) server does not guarantee data confidentiality in the presence of data inference channels [2][3][4] realized by the combination of queries. Note that data inference is the process of deducing forbidden information by devious means.…”

Section: Introductionmentioning

confidence: 99%

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Data warehouses are environments used for data analysis and efficient decision making within companies. They are tools that allow the execution of complex and multidimensional queries. One of the security vulnerabilities that can be used by malicious users is data inference, which is the deduction of private information by devious means. In the present work, we tried to show that the existence of functional dependencies in the data can help to perform an inference attack by using supervised learning algorithms to infer private information. These algorithms are Support Vector Machine (SVM), Random Forest (RF), Bayesian Regularized Neural Network (BRNN) and K-Nearest Neighbors (K-NN). The BRNN provided a better performance in our study. This paper implements an inference attack using regression learning algorithms, studies different dependency situations in the data, and uses the combination of COUNT, SUM, AVG and STDEV queries. The use of several methods in this study allows the prevention of inferences when one of these methods is used by a malicious user. We managed to achieve this attack by detecting 09.12% inferences on all methods compared to BRNN whose realized inference rate is 03.94%.

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