Biblio
The concept of the adversary model has been widely applied in the context of cryptography. When designing a cryptographic scheme or protocol, the adversary model plays a crucial role in the formalization of the capabilities and limitations of potential attackers. These models further enable the designer to verify the security of the scheme or protocol under investigation. Although being well established for conventional cryptanalysis attacks, adversary models associated with attackers enjoying the advantages of machine learning techniques have not yet been developed thoroughly. In particular, when it comes to composed hardware, often being security-critical, the lack of such models has become increasingly noticeable in the face of advanced, machine learning-enabled attacks. This paper aims at exploring the adversary models from the machine learning perspective. In this regard, we provide examples of machine learning-based attacks against hardware primitives, e.g., obfuscation schemes and hardware root-of-trust, claimed to be infeasible. We demonstrate that this assumption becomes however invalid as inaccurate adversary models have been considered in the literature.
KP-ABE mechanism emerges as one of the most suitable security scheme for asymmetric encryption. It has been widely used to implement access control solutions. However, due to its expensive overhead, it is difficult to consider this cryptographic scheme in resource-limited networks, such as the IoT. As the cloud has become a key infrastructural support for IoT applications, it is interesting to exploit cloud resources to perform heavy operations. In this paper, a collaborative variant of KP-ABE named C-KP-ABE for cloud-based IoT applications is proposed. Our proposal is based on the use of computing power and storage capacities of cloud servers and trusted assistant nodes to run heavy operations. A performance analysis is conducted to show the effectiveness of the proposed solution.
Since the first whole-genome sequencing, the biomedical research community has made significant steps towards a more precise, predictive and personalized medicine. Genomic data is nowadays widely considered privacy-sensitive and consequently protected by strict regulations and released only after careful consideration. Various additional types of biomedical data, however, are not shielded by any dedicated legal means and consequently disseminated much less thoughtfully. This in particular holds true for DNA methylation data as one of the most important and well-understood epigenetic element influencing human health. In this paper, we show that, in contrast to the aforementioned belief, releasing one's DNA methylation data causes privacy issues akin to releasing one's actual genome. We show that already a small subset of methylation regions influenced by genomic variants are sufficient to infer parts of someone's genome, and to further map this DNA methylation profile to the corresponding genome. Notably, we show that such re-identification is possible with 97.5% accuracy, relying on a dataset of more than 2500 genomes, and that we can reject all wrongly matched genomes using an appropriate statistical test. We provide means for countering this threat by proposing a novel cryptographic scheme for privately classifying tumors that enables a privacy-respecting medical diagnosis in a common clinical setting. The scheme relies on a combination of random forests and homomorphic encryption, and it is proven secure in the honest-but-curious model. We evaluate this scheme on real DNA methylation data, and show that we can keep the computational overhead to acceptable values for our application scenario.