Emc Corporation

Emc IP Holding Company LLC

Yariv Amar

Alex Zaslavsky

Oded Peer

Idan Aharoni

Mikael Peres

Tomer Meidan

Shachar Israeli

Daniel T Cohen

Parallel processing of events having multi-dimensional dependencies is provided. Each event has at least one profile and each profile has at least one profile key. An event is dependent on another event if they have at least one common profile key. The profile key(s) for a plurality of profiles of the given event are compared to profile keys of previously queued events. A given event is assigned to a same queue as at least one dependent event. When a given event is dependent on events in at least two queues, the given event is not assigned to a queue until only one queue has dependent events remaining to be processed. A list of profile keys that have been assigned to each queue and/or a reference count of a number of events that have been queued for each profile key are optionally maintained.

Parallel processing of events having multi-dimensional dependencies

A technique shares smart television data among subscribing organizations to provide security. The technique involves collecting, by an electronic server apparatus, data elements from multiple smart television devices. The technique further involves performing, by the electronic server apparatus, a set of risk analysis operations to generate risk scores corresponding to the multiple smart television devices. Each risk score (e.g., a numerical value) indicates an amount of risk (e.g., a probability) that a respective smart television device is malicious. The technique further involves providing, by the electronic server apparatus, an ordered list of the multiple smart television devices, the ordered list ranking the multiple television devices based on the risk scores. Information from the ordered list is well suited for use by an anti-fraud service in which subscriber organizations are informed of the information and use the information to identify and stop fraudulent activity in the future.

Smart television data sharing to provide security

Content randomization techniques for thwarting malicious software attacks. In one example, a method comprises the following steps. Content is received at a randomizer module from a first computing device, the content having been retrieved by the first computing device in response to a content request by a second computing device. The content is randomly altered at the randomizer module to generate randomly altered content. Log information about the random alteration to the content is maintained at the randomizer module. The randomly altered content is sent from the randomizer module to the first computing device such that the first computing device is able to provide the randomly altered content to the second computing device in response to the content request by the second computing device. Further, the random alteration may be removed from reply content using the log information.

Content randomization for thwarting malicious software attacks

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Ramla, Israel

Yariv Amar