Game Theory Based Correlated Privacy Preserving Analysis in Big Data

Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors’ privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments

Combined cloud:a mixture of voluntary cloud and reserved instance marketplace

Voluntary cloud is a new paradigm of cloud computing.It provides an alternative selection along with some well-provisioned clouds.However,for the uncertain time span that participants share their computing resources in voluntary cloud,there are some challenging issues,i.e.,fluctuation,under-capacity and low-benefit.In this paper,an architecture is first proposed based on Bittorrent protocol.In this architecture,resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle.Actually,these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud.Then,the fault rate of each node is used to evaluate the uncertainty of its sharing time.By leveraging a linear prediction model,it is enabled by a distribution function which is used for evaluating the computing capacity of the system.Moreover,the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud.At last,the system performance is validated by two sets of simulations.And the experimental results show the effectiveness of our computational method for resource reservation optimization

Involvement of TNF-alpha and IL-10 in breast cancer and patient survival

Purpose: To investigate the involvement of tumor necrosis factor α (TNF-α) and interleukin 10 (IL-10) in the pathogenesis of breast cancer in vivo as well as the activity of ten Chinese herbal compounds in human breast cancer (MCF-7) cell proliferation in vitro.Methods: In the in vivo study, the association of serum TNF-α and IL-10 with breast cancer cell invasiveness and prognosis was determined in female patients (n = 192) with breast cancer, while in the in vitro study, ten herbal Chinese compounds were screened for their effectiveness against MCF-7 cells. The levels of TNF-α, IL-10, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2/neu) were assayed using their respective enzyme-linked immunosorbent assay (ELISA) kits. Molecular docking was used to determine the lead compound(s) that can effectively inhibit TNF-α and IL-10.Results: Raised serum levels of TNF-α and IL-10 were significantly associated with breast cancer cell invasiveness and poor prognosis (p < 0.05). Moreover, there was a strong association between breast cancer prognosis and the expression levels of ER, PR and HER2/neu. Serum TNF-α and IL-10 levels were significantly elevated in stages II and III patients and in those with lymph node metastasis. Treatment of MCF-7 cells with the herbal compounds significantly reduced the synthesis and release of TNF-α and IL-10 (p < 0.05). The results of molecular docking showed that baicalein and oridonin significantly inhibited TNF- α and IL-10. The two herbal compounds had the highest docking scores for inhibition of cytokines, as well as favorable interaction energies.Conclusion: These results indicate that TNF-α and IL-10 are involved in the pathogenesis of breast cancer, and that baicalein and oridonin effectively inhibit the proliferation of the cells. Keywords: Baicalein, Breast cancer, Interleukin 10, Oridonin, Tumor necrosis factor alph

Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware analysis can be achieved through contrastive learning. We take the Android malware classification as an example to demonstrate our analysis. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and benign apps (or other types of malware). Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps are obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two widely used datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining 98.2% true positive rate on classifying 8,112 obfuscated malware samples

Towards Low Delay Sub-Stream Scheduling

Peer-to-Peer streaming is an effectual and promising way to distribute media content. In a mesh-based system, pull method is the conventional scheduling way. But pull method often suffers from long transmission delay. In this paper, we present a novel sub-stream-oriented low delay scheduling strategy under the push-pull hybrid framework. First the sub-stream scheduling problem is transformed into the matching problem of the weighted bipartite graph. Then we present a minimum delay, maximum matching algorithm. Not only the maximum matching is maintained, but also the transmission delay of each sub-stream is as low as possible. Simulation result shows that our method can greatly reduce the transmission delay

First Total Synthesis of a Naturally Occurring Iodinated 5′-Deoxyxylofuranosyl Marine Nucleoside

4-Amino-7-(5′-deoxy-β-D-xylofuranosyl)-5-iodo-pyrrolo[2,3-d]pyrimidine 1, an unusual naturally occurring marine nucleoside isolated from an ascidan, Diplosoma sp., was synthesized from D-xylose in seven steps with 28% overall yield on 10 g scale. The key step was Vorbrüggen glycosylation of 5-iodo-pyrrolo[2,3-d]pyrimidine with 5-deoxy-1,2-O-diacetyl-3-O-benzoyl-D-xylofuranose. Its absolute configuration was confirmed