SUMMARY: Lack of data integration remains a significant impediment to cancer research, and many analyses still require customized software to transform and prepare cancer data. We describe a software package to harmonize genetic and clinical cancer data into the GA4GH Phenopacket schema, an ISO standard for representing clinical case data. We integrated demographic, mutation, morphology, diagnosis, intervention, and survival data using case data from the National Cancer Institute for 12 cancer types. The Phenopacket standard provides a foundation for downstream use, including sophisticated statistical and AI/ML analyses. We demonstrate fitness for purpose by using the integrated data to recapitulate a known association between mutations in the gene encoding isocitrate dehydrogenase 1 and survival time in brain cancer patients. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at: https://github.com/monarch-initiative/oncopacket (archived at 10.5281/zenodo.15353125).

BACKGROUND: Next-generation sequencing platforms allow us to sequence millions of small fragments of DNA simultaneously, revolutionizing cancer research. Sequence analysis has revealed that cancer driver genes operate across multiple intricate pathways and networks with mutations often occurring in a mutually exclusive pattern. Currently, low-frequency mutations are understudied as cancer-relevant genes, especially in the context of networks. RESULTS: Here we describe a tool, gcMECM, that enables us to visualize the functionality of mutually exclusive genes in the subnetworks derived from mutation associations, gene-gene interactions, and graph clustering. These subnetworks have revealed crucial biological components in the canonical pathway, especially those mutated at low frequency. Examining the subnetwork, and not just the impact of a single gene, significantly increases the statistical power of clinical analysis and enables us to build models to better predict how and why cancer develops. CONCLUSIONS: gcMECM uses a computationally efficient and scalable algorithm to identify subnetworks in a canonical pathway with mutually exclusive mutation patterns and distinct biological functions.

System call checking is extensively used to protect the operating system kernel from user attacks. However, existing solutions such as Seccomp execute lengthy rule-based checking programs against system calls and their arguments, leading to substantial execution overhead.To minimize checking overhead, this paper proposes Draco, a new architecture that caches system call IDs and argument values after they have been checked and validated. System calls are first looked-up in a special cache and, on a hit, skip all checks. We present both a software and a hardware implementation of Draco. The latter introduces a System Call Lookaside Buffer (SLB) to keep recently-validated system calls, and a System Call Target Buffer to preload the SLB in advance. In our evaluation, we find that the average execution time of macro and micro benchmarks with conventional Seccomp checking is 1.14× and 1.25× higher, respectively, than on an insecure baseline that performs no security checks. With our software Draco, the average execution time reduces to 1.10× and 1.18× higher, respectively, than on the insecure baseline. With our hardware Draco, the execution time is within 1% of the insecure baseline.

A large percentage of real-world software configuration issues, such as misconfigurations, involve multiple interdependent configuration parameters. However, existing techniques and tools either do not consider dependencies among configuration parameters— termed configuration dependencies—or rely on one or two dependency types and code patterns as input. Without rigorous understanding of configuration dependencies, it is hard to deal with many resulting configuration issues.

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

Last-mile geo-localization plays an essential role in many location-based services, such as fraud detection and targeted advertising. In this study, we point out that round trip time (RTT) latency shows an extremely weak correlation with physical distance estimation in China's Internet, since a path between a vantage point and a destination can often be circuitous and inflated by queuing and processing delays. To sidestep the latency measurement, we perform a three-tier hop count based IP geo-localization mapping for China's Internet, on the assumption that each provincial router only serves a limited area. The mapping approach begins at the first tier using a single vantage point to fetch large-scale traceroute paths from the server to landmarks and target IPs. At the second tier, we try to find the last common routers along the traceroute paths of targets and landmarks and aggregate their hop count distances. At the third tier, we estimate the physical distances from hop count distances and provincial router radii, and geo-localize the targets to the nearest landmarks. Through large-scale experiments, we show that our approach is both cost-efficient and reliable, and can achieve last-ten-kilometer IP geo-localization for approximately 65% of the total 48874 pingable target IP addresses with a single ping server, and our hop count based approach completely outperforms the RTT based method.

As smart phones gradually become the dominant network traffic generators, app traffic analysis methods have gained great interests for network management and targeted advertisement. Specifically, previous works have shown that the scalability of app inference via traffic meta-data has the edge over traditional payload based analysis. However, such works mainly considered the ideal inference scenario where only one app is running on the client's device, without any background traffic noise interfered. In this paper, we extend the research to a more practical scenario, by assuming that multiple apps simultaneously run on a smart phone in the noisy background with complex traffic generated by the operating system. To that end, we propose APPCLASSIFIER, an Android app fingerprinting scheme for real-time app inference. We first leverage the observed differences in packet size distributions and traffic sequential behaviors to boost inference accuracy for noise-free traffic analysis. We then propose novel heuristic based methods to re-correct mislabeled traffic flows to realize real-time traffic inference. As a result, APPCLASSIFIER achieves inference accuracy of 82.3% for noise-free traffic analysis and reduces error rate from 66.7% to 36.4% for real-time traffic inference.

BACKGROUND: High-throughput sequencing has rapidly become an essential part of precision cancer medicine. But validating results obtained from analyzing and interpreting genomic data remains a rate-limiting factor. The gold standard, of course, remains manual validation by expert panels, which is not without its weaknesses, namely high costs in both funding and time as well as the necessarily selective nature of manual validation. But it may be possible to develop more economical, complementary means of validation. In this study we employed four synthetic data sets (variants with known mutations spiked into specific genomic locations) of increasing complexity to assess the sensitivity, specificity, and balanced accuracy of five open-source variant callers: FreeBayes v1.0, VarDict v11.5.1, MuTect v1.1.7, MuTect2, and MuSE v1.0rc. FreeBayes, VarDict, and MuTect were run in bcbio-next gen, and the results were integrated into a single Ensemble call set. The known mutations provided a level of "ground truth" against which we evaluated variant-caller performance. We further facilitated the comparison and evaluation by segmenting the whole genome into 10,000,000 base-pair fragments which yielded 316 segments. RESULTS: Differences among the numbers of true positives were small among the callers, but the numbers of false positives varied much more when the tools were used to analyze sets one through three. Both FreeBayes and VarDict produced strikingly more false positives than did the others, although VarDict, somewhat paradoxically also produced the highest number of true positives. The Ensemble approach yielded results characterized by higher specificity and balanced accuracy and fewer false positives than did any of the five tools used alone. Sensitivity and specificity, however, declined for all five callers as the complexity of the data sets increased, but we did not uncover anything more than limited, weak correlations between caller performance and certain DNA structural features: gene density and guanine-cytosine content. Altogether, MuTect2 performed the best among the callers tested, followed by MuSE and MuTect. CONCLUSIONS: Spiking data sets with specific mutations -single-nucleotide variations (SNVs), single-nucleotide polymorphisms (SNPs), or structural variations (SVs) in this study-at known locations in the genome provides an effective and economical way to compare data analyzed by variant callers with ground truth. The method constitutes a viable alternative to the prolonged, expensive, and noncomprehensive assessment by expert panels. It should be further developed and refined, as should other comparatively "lightweight" methods of assessing accuracy. Given that the scientific community has not yet established gold standards for validating NGS-related technologies such as variant callers, developing multiple alternative means for verifying variant-caller accuracy will eventually lead to the establishment of higher-quality standards than could be achieved by prematurely limiting the range of innovative methods explored by members of the community.

Usage-based Insurance (UBI) is regarded as a promising way to offer more accurate insurance premium by profiling driving behaviors. Compared with traditional insurance which considers drivers' history of accidents, traffic violations and etc, UBI focuses on driving data and can give a more reasonable insurance premium based on the current driving behaviors. Insurers use sensors in smartphone or vehicle to collect driving data (e.g. mileage, speed, hark braking) and compute a risk score based on these data to recalculate insurance premium. Many insurance programs, which are advertised as being privacy-preserving, do not directly use the GPS-based tracking, but it is not enough to protect driver's location privacy. In real world, many environment factors such as real-time traffic and traffic regulations can influence driving speed. These factors provide the side-channel information about the driving route, which can be exploited to infer the vehicle's trace. Based on the observation, we propose a novel speed based trajectory inference algorithm which can track drivers only with the speed data and original location. We implement the attack on a public dataset in New Jersey. The evaluation results show that the attacker can recover the route with a high successful rate.