Biblio

Big data will bring revolutionary changes from life to thinking for society as a whole. At the same time, the massive data and potential value of big data are subject to many security risks. Aiming at the above problems, a data privacy protection model for big data platform is proposed. First, the data privacy protection model of big data for data owners is introduced in detail, including protocol design, logic design, complexity analysis and security analysis. Then, the query privacy protection model of big data for ordinary users is introduced in detail, including query protocol design and query mode design. Complexity analysis and safety analysis are performed. Finally, a stand-alone simulation experiment is built for the proposed privacy protection model. Experimental data is obtained and analyzed. The feasibility of the privacy protection model is verified.

Insider attacks are one of the most dangerous threats to an organization. Unfortunately, they are very difficult to foresee, detect, and defend against due to the trust and responsibilities placed on the employees. In this paper, we first define the notion of user intent, and construct a model for the most common threat scenario used in the literature that poses a very high risk for sensitive data stored in the organization's database. We show that the complexity of identifying pseudo-intents of a user is coNP-Complete in this domain, and launching a harvester insider attack within the boundaries of the defined threat model takes linear time while a targeted threat model is an NP-Complete problem. We also discuss about the general defense mechanisms against the modeled threats, and show that countering against the harvester insider attack model takes quadratic time while countering against the targeted insider attack model can take linear to quadratic time depending on the strategy chosen. Finally, we analyze the adversarial behavior, and show that launching an attack with minimum risk is also an NP-Complete problem.

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.