The extraction of pixel-wise visual multi-cues for AHP-based privacy measurement

Cloud services consumers deal with a major challenge in selecting services from several providers. Facilitating these choices has become critical, and an important factor is the service trustworthiness. To be trusted by users, cloud providers should explicitly communicate their capabilities to ensure important functional and non-functional requirements (such as security, privacy, dependability, fairness, among others). Thus, models and mechanisms are required to provide indicators that can be used to support clients on choosing high quality services. This paper presents a solution to support privacy measurement and analysis, which can help the computation of trustworthiness scores. The solution is composed of a reference model for trustworthiness, a privacy model instance, and a privacy monitoring and assessment component. Finally, we provide an implementation capable of monitoring privacy-related information and performing analysis based on privacy scores for eight different datasets.

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A Privacy Measurement Method Using Network Structure Entropy

2017 International Conference on Networking and Network Applications (NaNA) ◽

10.1109/nana.2017.32 ◽

2017 ◽

Cited By ~ 1

Author(s):

Wuchao Shi ◽

Jing Hu ◽

Jun Yan ◽

Zhenqiang Wu ◽

Laifeng Lu

Keyword(s):

Network Structure ◽

Measurement Method ◽

Privacy Measurement

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OPriv: Optimizing Privacy Protection for Network Traffic

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10030038 ◽

2021 ◽

Vol 10 (3) ◽

pp. 38

Author(s):

Louma Chaddad ◽

Ali Chehab ◽

Ayman Kayssi

Keyword(s):

Network Traffic ◽

Privacy Protection ◽

Network Performance ◽

Traffic Analysis ◽

Real World Data ◽

Information Theoretic ◽

Non Linear Programming ◽

Flow Information ◽

Secure Network ◽

Privacy Measurement

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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