Microsoft Technology Licensing, LLC

Ajay Manchepalli

Sandeep P Karanth

Balasubramanyan Ashok

Satyanarayana V Lokam

Uppinakuduru Raghavendra Udupa

Santosh Raju Vysyaraju

Raghav Bhaskar

Debapratim De

Chinmay Eishan Kulkarni

A method and system for cryptographically indexing, searching for, and retrieving documents is provided. In some embodiments, an encryption system is provided that generates a document index that allows users to retrieve documents by performing encrypted queries for keywords associated with the documents. In some embodiments, each keyword maps to the same number of encrypted document identifiers. In some embodiments, an extractor graph is employed to map an indication of each keyword to a number of buckets storing encrypted document identifiers. In some embodiments, an order-preserving encryption system is provided. The encryption system uses an ordered index that maps encrypted instances of ordered attribute values to documents that are associated with those values. The ordered index enables queries containing query operators that rely on order, such as less than ('&#x3c;') or greater than ('&#x3e;'), to be successfully performed on encrypted attribute values.

Extending random number summation as an order-preserving encryption scheme

Techniques for representing keywords in an encrypted search index to prevent histogram-based attacks

Browsing sequence phrase identification technique embodiments are presented that generally extract topically-related phrases from the pages visited by a user in a browsing session. The topically-related phrases can be used for a variety of purposes, including aiding a user in re-finding previously visited sites. This phrase identification task is performed by considering not just the pages of a user's browsing sequence individually, but also pages visited immediately before and immediately after each page. In this way, phrases found in a page can be analyzed in the context in which the page was viewed, rather than in isolation. The identified phrases are further filtered by picking those that appear on a pre-populated topic list, and then clustering to find the most informative ones.

Identifying topically-related phrases in a browsing sequence

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Bengaluru, India

Ajay Manchepalli