Machine learning techniques have been widely applied to various applications. However, they are potentially vulnerable to data poisoning attacks, where sophisticated attackers can disrupt the learning procedure by injecting a fraction of malicious samples into the training dataset. Existing defense techniques against poisoning attacks are largely attack-specific: they are designed for one specific type of attacks but do not work for other types, mainly due to the distinct principles they follow. Yet few general defense strategies have been developed. In this paper, we propose De-Pois, an attack-agnostic defense against poisoning attacks. The key idea of De-Pois is to train a mimic model the purpose of which is to imitate the behavior of the target model trained by clean samples. We take advantage of Generative Adversarial Networks (GANs) to facilitate informative training data augmentation as well as the mimic model construction. By comparing the prediction differences between the mimic model and the target model, De-Pois is thus able to distinguish the poisoned samples from clean ones, without explicit knowledge of any ML algorithms or types of poisoning attacks. We implement four types of poisoning attacks and evaluate De-Pois with five typical defense methods on different realistic datasets. The results demonstrate that De-Pois is effective and efficient for detecting poisoned data against all the four types of poisoning attacks, with both the accuracy and F1-score over 0.9 on average.

Smart contracts, one of the success stories in blockchain 2.0, have been widely utilized in a broad range of applications, including those involving Internet of Things (IoT). Given the fast-pace nature of the topic, it can be challenging for the research community to keep track of the latest advances. Hence, in this article, we perform a comprehensive, in-depth review of known security challenges (e.g., inherently vulnerable particularities, programming vulnerabilities, and attacks) and potential research opportunities associated with the deploying of smart contracts in an IoT setting. We hope this survey will serve as a starting point for the readers seeking to understand and explore the potential applications of smart contracts.

Vascular calcification is highly prevalent in end-stage renal diseases and is predictive of cardiovascular events and mortality. Poly(ADP-ribose) polymerase 1 (PARP1) inhibition or deletion is vasoprotective in several disease models. Here we show that PARP activity is increased in radial artery samples from patients with chronic renal failure, in arteries from uraemic rats, and in calcified vascular smooth muscle cells (VSMCs) in vitro. PARP1 deficiency blocks, whereas PARP1 overexpression exacerbates, the transdifferentiation of VSMCs from a contractile to an osteogenic phenotype, the expression of mineralization-regulating proteins, and calcium deposition. PARP1 promotes Runx2 expression, and Runx2 deficiency offsets the pro-calcifying effects of PARP1. Activated PARP1 suppresses miRNA-204 expression via the IL-6/STAT3 pathway and thus relieves the repression of its target, Runx2, resulting in increased Runx2 protein. Together, these results suggest that PARP1 counteracts vascular calcification and that therapeutic agents that influence PARP1 activity may be of benefit to treat vascular calcification.

With the increasing popularity of location-based services (LBS), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region (CR) via a trusted third-party anonymiser. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users. One inherent limitation of k-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIA) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymiser, the probability of disclosing one’s location privacy could be greatly more than 1/k, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables k-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based k-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.