Abstract: A method for facilitating optimization of a cluster computing network for sequencing data analysis using adaptive data parallelization is provided. The method comprises the following steps. (a) A data parallelization configuration is determined, based on sequencing data and a pipeline selection, wherein the data parallelization configuration includes partition indication data indicating at least one biological information unit based on which of the sequencing data is to be partitioned.

Abstract: Methods and systems for high-throughput sequencing data analysis are provided. In an embodiment, the method includes the following steps. An input DNA/RNA/Protein sequence is received by a master computing unit. The input DNA/RNA/Protein sequence is partitioned into overlapping segments with a sliding window less than a segment length of the overlapping segments to allow overlapping of any successive two thereof by the master computing unit. The overlapping segments are distributed by the master computing unit to a plurality of slave computing units in a cloud computing environment. Suffix-expansion-sorting processing is performed on the overlapping segments by the slave computing units to produce sorted expansion segments. Distributed database tables are generated based on the sorted expansion segments by at least a portion of the slave computing units.

Abstract: Methods and systems for high-throughput sequencing data analysis are provided. In an embodiment, the method includes the following steps. An input DNA/RNA/Protein sequence is received by a master computing unit. The input DNA/RNA/Protein sequence is partitioned into overlapping segments with a sliding window less than a segment length of the overlapping segments to allow overlapping of any successive two thereof by the master computing unit. The overlapping segments are distributed by the master computing unit to a plurality of slave computing units in a cloud computing environment. Suffix-expansion-sorting processing is performed on the overlapping segments by the slave computing units to produce sorted expansion segments. Distributed database tables are generated based on the sorted expansion segments by at least a portion of the slave computing units.

Abstract: Trap data is stored in a mobile computing device. A lightweight engine monitors outgoing communications in the mobile computing device for the trap data. Data leakage is deemed to have been detected in the mobile computing device when an outgoing communication includes the trap data. A cloud service hosted by a backend system may also be monitoring for the trap data. In the case where the trap data is a trap e-mail address, the cloud service may monitor for an e-mail that is addressed to the trap e-mail address. In response to receiving the e-mail, the cloud service may consult a database to determine that the trap e-mail address is stored in the mobile computing device. Receiving the e-mail indicates that data leakage is occurring in the mobile computing device.

Abstract: A low resource mobile device, such as a smart phone or a tablet running a mobile operating system, requests a cloud computer system to inspect a mobile application for malicious content. The cloud computer system downloads the mobile application from a mobile application source, and installs the mobile application in a virtual machine sandbox. The cloud computer system inspects the mobile application for malicious content while the mobile application executes in the virtual machines sandbox. The result of the inspection is sent to the user in accordance with a setting that may be indicated in a cloud sandbox agent running on the mobile device.

Abstract: A data access policy is configured and stored on a computing device, including a list of secure gateway IP addresses and optionally secure geographic regions. A time parameter defines how long a digital file will remain not in use before deletion and a degree parameter defines how fast the file will be deleted. Once a digital file is downloaded to the computing device the device is checked periodically to determine whether or not it is in a secure location. If not in a secure location then a data deletion process is initiated which begins by checking whether or not the digital file is currently being used on the computing device. If the file is being used, then no deletion is performed. If the file is not in use (or has not been used after a certain amount of time) then the file is deleted. The file may be deleted gradually.

Abstract: A trap is dynamically created in a computing device to detect a malicious application. The trap may be a fake e-mail address created in response to detecting running of an application in the computing device. The fake e-mail address includes a local-part that identifies the application and identifies the mobile computing device (e.g., by user identifier). A backend system receives e-mails that are addressed to fake e-mail addresses. The backend system parses a recipient address of a received e-mail to identify an application associated with the e-mail and the computing device where the fake e-mail address was generated. The backend system informs a user of the computing device of a data leakage occurring in the computing device and the application that may be responsible for the data leakage.

Abstract: A system for locally detecting computer security threats in a computer network includes a processing engine, a fingerprint engine, and a detection engine. Data samples are received in the computer network and grouped by the processing engine into clusters. Clusters that do not have high false alarm rates are passed to the fingerprint engine, which generates fingerprints for the clusters. The detection engine scans incoming data for computer security threats using the fingerprints.