Microsoft Technology Licensing, LLC

Ehud Wieder

Kunal Talwar

Nir Nice

Rina Panigrahy

Arie Friedman

Lincoln K Uyeda

Michael Isard

Yacov Yacobi

Vijayan Prabhakaran

Jonathan James Currey

Boaz Feldbaum

Sefy Ophir

Eran Shamir

Embodiments of the present invention relate to systems, methods, and computer-storage media for maintaining privacy while delivering advertisements based on encrypted user profile identifiers. In embodiments, a Public key Encryption with Keyword Search (PEKS) is used to generate a public key and a private key. In embodiments, a public key and a private key are used to encrypt user profile identifiers and generate trapdoors associated with defined profile identifiers, respectively. A portion of the encrypted user profile identifiers are compared to a portion of the trapdoors. If a match is present between at least one encrypted user profile identifier and an associated trapdoor, a delivery engine is provided with an identification of content to be delivered to the user. The provided description is then used to determine an advertisement to present to a user. The advertisement is then presented to the user.

Maintaining privacy during personalized content delivery

A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Virtual machine packing method using scarcity

Systems, methods, and computer storage media having computer-executable instructions embodied thereon that maintain privacy during user profiling are provided. A profiling service receives, from a first device, rules for profiling a user. The rules were encrypted using a private key. The profiling service also receives, from a second device, user data. The user data was encrypted using a public key communicated to the second device by the first device. The profiling service then matches the encrypted rules with the encrypted user data, and based on the matching, generates a profile for the user. In embodiments, such a user profile can be utilized to deliver personalized digital content to a user.

Maintaining privacy during user profiling

A job scheduler may schedule concurrent distributed jobs in a computer cluster by assigning tasks from the running jobs to compute nodes while balancing fairness with efficiency. Determining which tasks to assign to the compute nodes may be performed using a network flow graph. The weights on at least some of the edges of the graph encode data locality, and the capacities provide constraints that ensure fairness. A min-cost flow technique may be used to perform an assignment of the tasks represented by the network flow graph. Thus, online task scheduling with locality may be mapped onto a network flow graph, which in turn may be used to determine a scheduling assignment using min-cost flow techniques. The costs may encode data locality, fairness, and starvation-freedom.

Scheduling ready tasks by generating network flow graph using information receive from root task having affinities between ready task and computers for execution

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Sunnyvale, CA, United States of America

Ehud Wieder