Microsoft Technology Licensing, LLC

Sap Se

Sap Ag

Bea Systems, Inc.

Amadeus S.a.s.

28msec Inc.

Donald Kossmann

Arvind Arasu

Kenneth Hiroshi Eguro

Raghav Kaushik

Norman May

Ravishankar Ramamurthy

Martin Kaufmann

Badrish Chandramouli

Manas Rajendra Joglekar

Panagiotis Antonopoulos

Andreas Tonder

Ravi Ramamurthy

Ramarathnam R Venkatesan

Jonathan David Goldstein

Anil Kumar Goel

Justin Jon Levandoski

Fabio Riccardi

Shriraghav Kaushik

Mike Barnett

Dietmar Fauser

Gustavo Alonso

Yinan Li

Paul J Lucas

Georgios Giannikis

Cedric Florimond

Philipp Unterbrunner

Daniela D Florescu

James Adam Hunter

Spyridon Blanas

Aditya Nigam

Nikhil Vithlani

Prasang Upadhyaya

Pingfan Meng

Vineet Pandey

Chang Ge

Jeremy Meyer

Gunaprasaad Jeganathan

Nikolaos Romanos Katsipoulakis

Christopher James Hillery

Juchang Lee

Per-Ake Larson

Franz Faerber

Cesar Alejandro Galindo-Legaria

Carsten Binnig

Michael I Zwilling

Joachim Hammer

Bala Neerumalla

Stefan Hildenbrand

Jakub J Szymaszek

Elias Yousefi Amin Abadi

Amin Amiri Manjili

Andreas Luethi

Matthias Brantner

David Graf

Tim Kraska

Puspanantha Shanmuganathan

Till Carsten Westmann

Kedar Dubhashi

Till Casten Westmann

Cédric Florimond

Deferred verification of the integrity of data operations over a set of data that is hosted at an untrusted module (UM) is controlled. The controlling includes generating a request for a data operation on the set of data. The request includes an authentication portion. The request is sent to the UM. A response to the request is received from the UM. The response includes cryptographic verification information attesting the integrity of the data operation with respect to prior data operations on the set of data. The response includes results from deferred verification at a trusted module (TM).

Controlling verification of key-value stores

A method includes obtaining a query containing at least one field from which data is being queried, obtaining a dataset having a schema-free data exchange format having multiple fields of data at different physical positions in the dataset, and parsing the dataset by obtaining a structural index that maps logical locations of fields to physical locations of the fields of the dataset, accessing the structural index with logical locations of the fields that index to the physical locations, and providing data from the fields based on the physical locations responsive to the query.

Parser for schema-free data exchange format

A system includes a processor and a memory storing multiple records in a key-value data structure and processor executable instructions for managing access to records in the key-value data structure. The instructions are executable to perform operations including managing a shared atomic epoch counter and thread epoch counters, determining a maximal safe epoch as a function of the shared atomic epoch counter and the thread epoch counters, maintaining a drain list of trigger actions, and triggering the trigger actions in the drain list as a function of an update of the shared atomic epoch counter and the maximal safe epoch.

Key-value store system

Partitioning of temporal databases can implement distributed storage of temporal data via time-based or space-based techniques to improve performance of operators on the database. A variety of operators can be supported for the partitioned tables, including temporal aggregation, time travel, and temporal join. The use of checkpoints can greatly increase performance in a variety of scenarios. The described partitioning techniques can be applied in a parallel execution context to great benefit. The partitioning can also reduce local memory footprint, facilitating in-memory database processing.

Timeline index for partitioned temporal database tables

Methods, systems, and computer-readable media are directed towards receiving, at an untrusted component, a query for a data store. The query includes a plurality of data operations. The data store is accessible by the untrusted component. A first proper subset of data operations is determined from the plurality of data operations that do not access sensitive data within the data store. A second proper subset of data operations is determined from the plurality of data operations that access sensitive data within the data store. The first proper subset of data operations is executed, at the untrusted component, to create first results. The second proper subset of data operations is sent to a trusted component for execution. Second results based on the sending the second proper subset of data operations are received from the trusted component. Results to the query are returned based on the first results and the second results.

Secure data processing on sensitive data using trusted hardware

A 'Database Confidentiality System' provides various techniques for using server-side trusted computing in combination with configurable type metadata and user- or system-definable rules associated with individual database fields to implement database confidentiality. In various implementations, type metadata and one or more rules are added to each database field. Metadata includes a domain, method of encryption, and a pointer to an encryption key used to encrypt the data in the corresponding field. The rules define one or more operations allowed on the corresponding data types. The type metadata and rules are optionally integrity protected and/or encrypted to avoid unauthorized changes or access. Various encryption techniques (e.g., probabilistic, Paillier, etc.) allow some computations to be performed in an untrusted environment without access to the encryption key. This enables the Database Confidentiality System to maintain database confidentiality while performing distributed computation and communications between the untrusted machine and the trusted machine.

Type-based database confidentiality using trusted computing

Computer systems, devices, and associated methods of evaluating an expression comprising restricted data are disclosed herein. In one embodiment, a method includes receiving a database statement from a client application and verifying the authenticity of the database statement. If the database statement is authentic, an approved expression is identified in the database statement for creating an evaluation rule. The method further includes restricting evaluation of expressions in a protected computing environment according to the created evaluation rule.

Expression evaluation of database statements for restricted data

Computer systems, devices, and associated methods of optimizing the execution of instructions of a database statement by a database server are disclosed herein. In one embodiment, a method includes identifying a potential execution plan for executing instructions of the database statement and estimating a cost for executing the execution plan. The cost can comprise an encrypted data processing cost associated with a operation in the execution plan of executing an operation on encrypted data in a protected computing environment. The method can include estimating the encrypted data processing cost in the protected computing environment based on statistics generated in the protected computing environment about a database table. In response to estimating the cost for executing the execution plan, comparing the cost to estimated costs of alternative execution plans, selecting the lowest-cost plan for execution, and executing the lowest-cost execution plan.

Execution optimization of database statements involving encrypted data

Calculation of aggregated values in a history database table can be optimized using an approach in which an ordered history table is accessed. The ordered history table can include a sequential listing of commit identifiers associated with updates, insertions, and/or deletions to values in the database table. The ordered history table can be traversed in a single pass to calculate an aggregation function using an optimized algorithm. The optimized algorithm can enable calculation of an aggregated metric of the values based on a selected method for tracking invalidated values to their corresponding commit identifiers. The calculated metric is generated for a current version of the database table; and promoted.

Time slider operator for temporal data aggregation

A number of transmissions of secure data communicated between a secure trusted device and an unsecure untrusted device in a DBMS is controlled. The data is communicated for database transaction processing in the secure trusted device. The number of transmissions may be controlled by receiving, from the untrusted device, an encrypted key value of a key and a representation of an index of a B-tree structure, decrypting, at the trusted device, the key and one or more encrypted index values, and initiating a transmission, a pointer value that identifies a lookup position in the index for the key. The index comprises secure, encrypted index values. Other optimizations for secure processing are also described, including controlling available computation resources on a secure trusted device in a DBMS and controlling transmissions of secure data that is communicated between a secure trusted device and an unsecure untrusted device in a DBMS.

Controlling secure processing of confidential data in untrusted devices

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Zurich, Switzerland

Donald Kossmann