In this paper, we reconsider the notion of plaintext awareness. We present a new model for plaintext-aware encryption that is both natural and useful. We achieve plaintext-aware encryption without random oracles by using a third party. However, we do not need to trust the third party: even when the third party is dishonest, we still guarantee security against adaptive chosen ciphertext attacks....

In network link prediction, it is possible to hide a target from being predicted with small perturbation on structure. This observation may be exploited in many real world scenarios, for example, preserve privacy, or exploit financial security. There have been recent studies generate adversarial examples mislead deep learning models graph data. However, none of the previous work has considered ...

Image denoising can remove natural noise that widely exists in images captured by multimedia devices due to low-quality imaging sensors, unstable image transmission processes, or low light conditions. Recent works also find benefits the high-level vision tasks, e.g., classification. In this work, we try challenge common sense and explore a totally new problem, i.e., whether be giv...

Attack trees allow a security analyst to obtain an overview of the potential vulnerabilities of a system. Due to their refinement structure, attack trees support the analyst in understanding the system vulnerabilities at various levels of abstraction. However, contrary to manually synthesized attack trees, automatically generated attack trees are often not refinement-aware, making subsequent hu...

Aware aggregation is an important subtask of Imposition detection. The goal is to identify and to cluster different Awares produced by low-level Imposition detection systems, firewalls, etc. Belonging to a specific attack instance which has been initiated by an attacker at a certain point in time. Thus, meta-Awares can be generated for the clusters that contain all the relevant information wher...

Existing model poisoning attacks on federated learning (FL) assume that an adversary has access to the full data distribution. In reality, usually limited prior knowledge about clients' data. A poorly chosen target class renders attack less effective. This work considers a semi-targeted situation where source is predetermined but not. The goal cause misclassification of global classifier from c...