Microsoft Technology Licensing, LLC

Jefferson Lai

Lin Xu

Parvez Ahammad

Sanjay Sachdev

Ryan M Rogers

Subbu Subramaniam

Adrian Rivera Cardoso

Arjun K Kulothungun

Rahul Tandra

Mark B Cesar

Yu Chen

Vinyas Maddi

Shraddha Sahay

Gavin Castro Uathavikul

Srikant Veera Raghavan

Neha Jain

Technologies for providing event-level data privacy for streaming post analytics data include, in some embodiments, receiving a data stream that includes instances of count data collected over a time interval, computing a true count breakdown that includes a set of sub-counts of non-public user interface interactions on the post, creating a noisy count breakdown by applying at least one differential privacy mechanism to the set of sub-counts, and streaming the noisy count breakdown instead of the true count breakdown to a computing device. At least one of the sub-counts is a count that is associated with a particular value of an attribute that has different possible values. The attribute is associated with the non-public user interface interactions on the post.

Event-level data privacy for streaming post analytics data

The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains a set of parameters associated with a query for a ranking of candidates for an opportunity, wherein the set of parameters include a candidate, the opportunity, and a ranking category. Next, the system uses the set of parameters to obtain a set of pre-computed scores for use in generating the ranking and determine a number of online scores required to complete the ranking. The system then generates a set of calls to produce the determined number of the online scores. Finally, the system uses the pre-computed scores and the online scores to generate a result of the query.

Scalable processing of queries for applicant rankings

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San Jose, CA, United States of America

Jefferson Lai