Biblio

Filters: Author is Zhang, Mengyuan  [Clear All Filters]
2021-05-05
Tabiban, Azadeh, Jarraya, Yosr, Zhang, Mengyuan, Pourzandi, Makan, Wang, Lingyu, Debbabi, Mourad.  2020.  Catching Falling Dominoes: Cloud Management-Level Provenance Analysis with Application to OpenStack. 2020 IEEE Conference on Communications and Network Security (CNS). :1—9.

The dynamicity and complexity of clouds highlight the importance of automated root cause analysis solutions for explaining what might have caused a security incident. Most existing works focus on either locating malfunctioning clouds components, e.g., switches, or tracing changes at lower abstraction levels, e.g., system calls. On the other hand, a management-level solution can provide a big picture about the root cause in a more scalable manner. In this paper, we propose DOMINOCATCHER, a novel provenance-based solution for explaining the root cause of security incidents in terms of management operations in clouds. Specifically, we first define our provenance model to capture the interdependencies between cloud management operations, virtual resources and inputs. Based on this model, we design a framework to intercept cloud management operations and to extract and prune provenance metadata. We implement DOMINOCATCHER on OpenStack platform as an attached middleware and validate its effectiveness using security incidents based on real-world attacks. We also evaluate the performance through experiments on our testbed, and the results demonstrate that DOMINOCATCHER incurs insignificant overhead and is scalable for clouds.

2019-12-30
Yang, Lei, Zhang, Mengyuan, He, Shibo, Li, Ming, Zhang, Junshan.  2018.  Crowd-Empowered Privacy-Preserving Data Aggregation for Mobile Crowdsensing. Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. :151–160.
We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which aims to address the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers' data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve a desirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, we explore the problem structure, characterize the hidden monotonicity property of the problem, and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can recruit a set of workers to approximately minimize the cost of purchasing private sensing data from workers subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.