Secure data outsourcing in presence of the inference problem: A graph-based approach

Jebali, Adel; Sassi, Salma; Jemai, Abderrazak; Chbeir, Richard

doi:10.1016/j.jpdc.2021.09.006

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The authors consider simple FDs between attributes, their solution does not consider metadata related to the processed queries, nor temporal or spatial constraints that user’s knowledge must satisfy to exploit a FD. Jebali et al [ 26 ] focus on data outsourcing and propose a solution which maintains access control policies of data owner while controlling inference leakage. To do so, they model the FDs graph corresponding to inference channels in the initial relational schema.…”

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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Section: Related Workmentioning

confidence: 99%

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study

Lachat

Bennani

Rehn‐Sonigo

et al. 2022

Sensors

View full text Add to dashboard Cite

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Auditing of Outsourced Data Integrity - A Taxonomy

Srivastava

Kumar

2023

EEENG

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INTRODUCTION: Cloud has gained a huge number of consumers in today's environment due to its broad range of services. On the cloud server, data owners can store any type of data, and users can access it whenever they need it. METHOD: Numerous challenges come with the ease of use of outsourced data. The accuracy and secrecy of data outsourced come at stake. Users have a low level of trust in cloud service providers since they can be deceptive at times. A cloud audit is an examination conducted by a data owner to assess and document the performance of their cloud vendor. RESULT: The goal of a cloud vendor audit is to determine how well they follow a set of predetermined controls. CONCLUSION: In the article, the authors present various schemes of cloud auditing, their categorizations, and merits and demerits along with the future directions of research in the domain.

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Hybrid Graph Partitioning with OLB Approach in Distributed Transactions

Bharati¹,

Attar²

2023

Intelligent Automation &Amp; Soft Computing

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Secure data outsourcing in presence of the inference problem: A graph-based approach

Cited by 3 publications

References 38 publications

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study

Auditing of Outsourced Data Integrity - A Taxonomy

Hybrid Graph Partitioning with OLB Approach in Distributed Transactions

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