Microsoft Technology Licensing, LLC

Other

Ravishankar Ramamurthy

Arvind Arasu

Kenneth Hiroshi Eguro

Surajit J Chaudhuri

Donald Kossmann

Vivek Ravindranath Narasayya

Raghav Kaushik

Manas Rajendra Joglekar

Ramarathnam R Venkatesan

Kaushik Shriraghav

Shriraghav Kaushik

Nicolas Bruno

Spyridon Blanas

Pingfan Meng

Vineet Pandey

Prasang Upadhyaya

Zsolt Istvan

Kaushik Shiriraghav

Hicham G Elmongui

Govind Kabra

Sundararajarao Sudarshan

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

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 relational database server may concurrently execute many relational queries, but a complex relational query may cause performance delays in the fulfillment of other relational queries. Instead, the relational database server may generate a query plan for the relational query, and may endeavor to partition the relational query between a spool operator and a scan operator into two or more query slices, where each query slice may be executed within a query slice threshold. Many alternative candidate query plans may be considered, such as inserting spool and scan operators after various operators and parameterizing operators in order to partition the records of a relation into two or more ranges based on an attribute of the relation. A large search space of candidate query plans may be reviewed in order to select a query plan that respects the query slice threshold while efficiently executing the logic of the relational query.

Slicing relational queries using spool operators

A database management system (DBMS) run a host CPU and a hardware coprocessor accelerate traversal of a tree-type data structure by allocating reusable memory in cache to store portions of the tree-type data structure as the tree-type data structure is being requested by the host CPU. The hardware coprocessor manages the cached tree-type data structure in a manner that is transparent to the host CPU. A driver located at the host CPU or at a separate computing device can provide an interface between the host CPU and the hardware coprocessor, thus reducing communications between the host CPU and the hardware coprocessor.

Systems and methods for providing distributed tree traversal using hardware-based processing

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

The techniques discussed herein facilitate the transmission, storage, and manipulation of data in an encrypted database management system (EDBMS). An untrusted machine is connected to a data store having encrypted records, a client machine that sends encrypted queries, and a trusted machine that receives and decrypts the encrypted records and encrypted queries. The trusted machine processes the query using semantically secure query operators to produce a query result. The trusted machine ensures the size of the query result conforms to an upper bound on the number or records in the query result and returns the query result.

Customized trusted computer for secure data processing and storage

The subject disclosure is directed towards secure query processing over encrypted database records without disclosing information to an adversary except for permitted information. In order to adapting semantic security to a database encryption scheme, a security model for all query processing is specified by a client and used to determine which information is permitted to be disclosed and which information is not permitted. Based upon the security model, a trusted, secure query processor transforms each query and an encrypted database into secure query results. Even though the adversary can view the secure query results during communication to the client, the adversary cannot determine any reliable information regarding the secure query results or the encrypted database.

Secure query processing over encrypted data

A cloud computing service to securely process queries on a database. A security device and method of operation are also disclosed. The security device may be provisioned with a private key of a subscriber to the cloud service and may have processing hardware that uses that key, sequestering the key and encryption processing in hardware that others, including operating personnel of the cloud service, cannot readily access. Processing within the security device may decrypt queries received from the subscriber and may encrypt responses for communication over a public network. The device may perform functions on clear text, thereby limiting the amount of clear text data processed on the cloud platform, while limiting bandwidth consumed in communicating with the subscriber. Such processing may include formatting data, including arguments in a query, in a security protocol used by the cloud platform.

Secure cloud database platform with encrypted database queries

Methods and systems for auditing queries using query differentials are disclosed. A method includes identifying a set of queries, determining if results of each query of the set of queries are different from results of respective differentials of each query of the set of queries, and based on the determining, making a characterization of each query of the set of queries as one of safe and unsafe. Access is provided to the characterization of each query of the set of queries.

Auditing queries using query differentials

Described is a test framework for testing transformation rules of query optimizers. Rule patterns obtained as tree structures from a query optimizer are used to generate queries that are used to test the rule optimizer's transformation rules. The test framework tracks which rules are exercised for each query, and also determines the correctness of the transformation rule by comparing the results of the query processing with the rule and without the rule (by turning off the rule). The test framework creates a composite pattern corresponding to two or more rules, such as to test rules in a set (e.g., as pairs). Also described is the efficient execution of a test suite for correctness testing, in which queries of the test suite are selected based upon cost information.

Framework for testing query transformation rules

A flexible query hints system and method for discovering and expressing query hints in a database management system. Embodiments of the flexible query hints system and method include a power hints (Phints) language that enables the specification of constraints to influence a query optimizer. Phints expressions are defined as tree patterns annotated with constraints. Embodiments of the flexible query hints system and method also include techniques to incorporate the power hints language expressions into an extended query optimizer. Theses techniques include computing a directed acyclic graph for Phints expression, deriving candidate matches using the Phints expression and the graph, computing candidate matches, and extracting a revised execution plan having a lowest cost and satisfying constraints of the Phints expression. Embodiments of the flexible query hints system and method include a flexible query hint user interface that allow users to interactively adjust query hints.

Flexible query hints in a relational database

An exact cardinality query optimization system and method for optimizing a query having a plurality of expressions to obtain a cardinality-optimal query execution plan for the query. Embodiments of the system and method use various techniques to shorten the time necessary to obtain the cardinality-optimal query execution plan, which contains the query execution plan when all cardinalities are exact. Embodiments of the system and method include a covering queries technique that leverages query execution feedback to obtain an unordered subset of relevant expressions for the query, an early termination technique that bounds the cardinality to determine whether the processing can be terminate before each of the expressions are executed, and an expressions ordering technique that finds an ordering of expressions that yields the greatest reduction in time to obtain the cardinality-optimal query execution plan.

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Redmond, WA, United States of America

Ravishankar Ramamurthy