A Dummy-Based Approach for Preserving Source Rate Privacy

Diyanat, Abolfazl; Khonsari, Ahmad; Shariatpanahi, Seyed Pooya

doi:10.1109/tifs.2016.2515050

Cited by 17 publications

(7 citation statements)

References 20 publications

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“…Dummy-based method is also a mechanism for protecting location privacy which was used in many Fig. 4 Example of the negative survey in protecting the location privacy [16] researches [19][20][21]. This approach has been proposed by Kido et al [21].…”

Section: Dummy-based Methodmentioning

confidence: 99%

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Privacy issues in social networks and analysis: a comprehensive survey

Pham

Sood

et al. 2018

IET Networks

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Social networks have become part of today's life; however, they also create numerous privacy problems for their users as they reveal sensitive information. Thus, the privacy preservation has become a big problem in social networks and there are several researches related to this topic. However, the current research achievements for this issue are only ad hoc solutions and the broad picture of the problem is not clear. This paper surveys a wide range of related researches and realises that the topic includes many sub-areas; for example, the privacy in publishing social network data for the use of third-party consumers or the privacy of users in the leakage of individuals' information to unexpected people in their social circle. The study also analyses the advantages as well as limitations of proposed solutions. On the basis of that the research outlines key outstanding issues and recommends directions for the future research. Moreover, the research also investigates common privacy metrics and popular privacy-preserving techniques to provide readers some basic tools to resolve open problems in the topic. The ultimate purpose of this research is to pave a solid background for people, who are interested in the topic, to do research further.

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Section: Dummy-based Methodmentioning

confidence: 99%

“…Dummy‐based method is also a mechanism for protecting location privacy which was used in many researches [19–21]. This approach has been proposed by Kido et al .…”

Section: Introductionmentioning

confidence: 99%

Privacy issues in social networks and analysis: a comprehensive survey

Pham

Sood

et al. 2018

IET Networks

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“…Maximum transmission unit (MTU) padding, for example, is a well-known mutation technique that consists of padding all the flow packets to the maximum payload size MTU [19]. Random padding is another technique that consists of randomly padding the packets [41].…”

Section: Literaturementioning

confidence: 99%

OPriv: Optimizing Privacy Protection for Network Traffic

Chaddad

Chehab

Kayssi

2021

JSAN

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Statistical traffic analysis has absolutely exposed the privacy of supposedly secure network traffic, proving that encryption is not effective anymore. In this work, we present an optimal countermeasure to prevent an adversary from inferring users’ online activities, using traffic analysis. First, we formulate analytically a constrained optimization problem to maximize network traffic obfuscation while minimizing overhead costs. Then, we provide OPriv, a practical and efficient algorithm to solve dynamically the non-linear programming (NLP) problem, using Cplex optimization. Our heuristic algorithm selects target applications to mutate to and the corresponding packet length, and subsequently decreases the security risks of statistical traffic analysis attacks. Furthermore, we develop an analytical model to measure the obfuscation system’s resilience to traffic analysis attacks. We suggest information theoretic metrics for quantitative privacy measurement, using entropy. The full privacy protection of OPriv is assessed through our new metrics, and then through extensive simulations on real-world data traces. We show that our algorithm achieves strong privacy protection in terms of traffic flow information without impacting the network performance. We are able to reduce the accuracy of a classifier from 91.1% to 1.42% with only 0.17% padding overhead.

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“…Augmented dummy data bears redundancy, requires extra bandwidth and buffer, and consumes more energy. Diyanat et al [175] minimally augment dummy data to preserve the (original) data privacy of a client without changing the statistical behavior of the original data, such as distribution. They minimize the weighted sum of augmented dummy communication cost and privacy degree.…”

Section: A Privacy Of Agentmentioning

confidence: 99%

“…Therefore, they require extra bandwidth and buffer, and consume more energy. As a result, it is critical to strike a balance between augmented dummy communication cost and privacy degree [175]. Encoding encrypts the collective features of data and sends the features to the server, rather than the raw data [176].…”

Section: Lessons Learned and Open Issues A Lessons Learnedmentioning

confidence: 99%

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications

Chen

et al. 2020

Preprint

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Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, scalability, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.

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A Dummy-Based Approach for Preserving Source Rate Privacy

Cited by 17 publications

References 20 publications

Privacy issues in social networks and analysis: a comprehensive survey

Privacy issues in social networks and analysis: a comprehensive survey

OPriv: Optimizing Privacy Protection for Network Traffic

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications

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