Inference Control in Distributed Environment: A Comparison Study

Jebali, Adel; Sassi, Salma; Jemai, Abderrazak

doi:10.1007/978-3-030-41568-6_5

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…A few solutions are proposed to detect inference attacks in a MC context as described by Jebali et al 16 To detect attacks at query-time, Chang et al 6 propose a solution to model probabilistic dependencies between distributed personal databases in the same system. Similarly to Chen et al's solution, Chang et al work does not consider TA B L E 1 Comparison of the existing solutions w.r.t the requirements (i)-(iv) described in Section 1.…”

Section: Solutions Targeting Several Databasesmentioning

confidence: 99%

Detecting inference attacks involving sensor data in a multi‐database context: Issues & challenges

Lachat

Bennani

Rehn‐Sonigo

et al. 2022

Internet Technology Letters

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Funding informationDeutsch-Französische Hochschule Nowadays applications produce and manage data of individual among which some may be sensitive and must be protected. Moreover, with the advent of smart applications, sensor data are produced by IoT devices in a huge quantity and sent to servers in the vicinity to be stored and processed. Meanwhile, newly discovered inference channels involving sensor data gives insights on personal data and raises new threats on individuals privacy. They escape the vigilance of traditional inference detection systems devoted to protecting personal data stored locally in a database. In this paper, we motivate the need of a distributed inference detection system acting in a general multi-database context and we highlight the issues that such a system would face.

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Section: Solutions Targeting Several Databasesmentioning

confidence: 99%

Detecting inference attacks involving sensor data in a multi‐database context: Issues & challenges

Lachat

Bennani

Rehn‐Sonigo

et al. 2022

Internet Technology Letters

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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%

Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Johnson¹,

Adépo²,

Kamagate³

et al. 2022

ISI

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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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“…In this article, we focus on protecting a single sensor database with an InfDS. Yet, a few InfDSs are proposed to detect inference attacks in a multi-database context, as described by Jebali et al [ 23 ]. For the shakes of completeness we present, briefly, the limitations that also exist in those works with regard to our requirements.…”

Section: Related Workmentioning

confidence: 99%

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study

Lachat

Bennani

Rehn‐Sonigo

et al. 2022

Sensors

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With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances’ energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset. As expected, detecting IASD is proven to be quadratic due to huge sensor data managed and a quickly growing amount of user knowledge. To overcome this drawback, we propose first a set of conceptual optimizations that reduces the detection complexity. Although becoming linear, as online detection time remains greater than a fixed acceptable query response limit, we propose two approaches to estimate the potential of RICE-Sy. The first one is based on partitioning strategies which aim at partitioning the knowledge of users. We observe that by considering the quantity of knowledge gained by a user as a partitioning criterion, the median detection time of RICE-Sy is reduced by 63%. The second approach is H-RICE-SY, a hybrid detection architecture built on RICE-Sy which limits the detection at query-time to users that have a high probability to be malicious. We show the limits of processing all malicious users at query-time, without impacting the query answer time. We observe that for a ratio of 30% users considered as malicious, the median online detection time stays under the acceptable time of 80 , for up to a total volume of 1.2 million user knowledge entities. Based on the observed growth rates, we have estimated that for 5% of user knowledge issued by malicious users, a maximum volume of approximately 8.6 million user’s information can be processed online in an acceptable time.

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Inference Control in Distributed Environment: A Comparison Study

Cited by 4 publications

References 38 publications

Detecting inference attacks involving sensor data in a multi‐database context: Issues & challenges

Detecting inference attacks involving sensor data in a multi‐database context: Issues & challenges

Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study

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