The last few years have witnessed a spate of data protection regulations in conjunction with an ever-growing appetite for data usage in large businesses, which presents significant challenges for businesses to maintain compliance. To address this conflict, we present Data Guard - a fine-grained, purpose-based access control system for large data warehouses. Data Guard enables authoring policies based on semantic descriptions of data and purpose of data access. Data Guard then translates these policies into SQL views that mask data from the underlying warehouse tables. At access time, Data Guard ensures compliance by transparently routing each table access to the appropriate data-masking view based on the purpose of the access, thus minimizing the effort of adopting Data Guard in existing applications. Our enforcement solution allows masking data at much finer granularities than what traditional solutions allow. In addition to row and column level data masking, Data Guard can mask data at the sub-cell level for columns with non-atomic data types such as structs, arrays, and maps. This fine-grained masking allows Data Guard to preserve data utility for consumers while ensuring compliance. We implemented a number of performance optimizations to minimize the overhead of data masking operations. We perform numerous experiments to identify the key factors that influence the data masking overhead and demonstrate the efficiency of our implementation. Data Guard is deployed inside LinkedIn's production data warehouses and ensures compliance of more than 20,000 table accesses each day across different data processing engines.

Strong member privacy in technology enterprises involves, among other objectives, deleting or anonymizing various kinds of data that a company controls. Those requirements are complicated in a heterogeneous data ecosystem where data is stored in multiple stores with different semantics: different indexing or update capabilities require processes specific to a store or even schema. In this demo we showcase a method to enforce record controls of arbitrary data stores via a three step process: generate an offline snapshot, run a policy mechanism to select rows to delete/update, and apply the changes to the original store. The first and third steps work on any store by leveraging Apache Gobblin, an open source data integration framework. The policy computation step runs as a batch Gobblin job where each table can be customized via a dataset metadata tracking system and SQL expressions providing table-specific business logic. This setup allows enforcement of highly-customizable privacy requirements in a variety of systems from hosted databases to third party data storage systems.

Many pervasive computing applications continuously monitor state changes in the environment by acquiring, interpreting and responding to information from sensors embedded in the environment. However, it is extremely difficult and expensive to obtain a continuous, complete, and consistent picture of a continuously evolving operating environment. One standard technique to mitigate this problem is to employ mathematical models that compute missing data from sampled observations thereby approximating a continuous and complete stream of information. However, existing models have traditionally not incorporated a notion of temporal validity, or the quantification of imprecision associated with inferring data values from past or future observations. In this paper, we support continuous monitoring of dynamic pervasive computing phenomena through the use of a series of snapshot queries. We define a decay function and a set of inference approaches to filling in missing and uncertain data in this continuous query.We evaluate the usefulness of this abstraction in its application to complex spatio-temporal pattern queries in pervasive computing networks.

Queries are convenient abstractions for the discovery of information and services, as they offer content-based information access. In distributed settings, query semantics are well-defined, for example, queries are often designed to satisfy ACID transactional properties. When query processing is introduced in a dynamic network setting, achieving transactional semantics becomes complex due to the open and unpredictable environment. In this article, we propose a query processing model for mobile ad hoc and sensor networks that is suitable for expressing a wide range of query semantics; the semantics differ in the degree of consistency with which query results reflect the state of the environment during query execution. We introduce several distinct notions of consistency and formally express them in our model. A practical and significant contribution of this article is a protocol for query processing that automatically assesses and adaptively provides an achievable degree of consistency given the operational environment throughout its execution. The protocol attaches an assessment of the achieved guarantee to returned query results, allowing precise reasoning about a query with a range of possible semantics. We evaluate the performance of this protocol and demonstrate the benefits accrued to applications through examples drawn from an industrial application.

In wireless sensor network applications, the potential to use cooperation to resolve user queries remains largely untapped. Efficiently answering a user's questions requires identifying the correct set of nodes that can answer the question and enabling coordination between them. In this article, we propose a query domain abstraction that allows an application to dynamically specify the nodes best suited to answering a particular query. Selecting the ideal set of heterogeneous sensors entails answering two fundamental questions— how are the selected sensors related to one another, and where should the resulting sensor coalition be located. We introduce two abstractions, the proximity function and the reference function , to precisely specify each of these concerns within a query. All nodes in the query domain must satisfy any provided proximity function, a user-defined function that constrains the relative relationship among the group of nodes (e.g., based on a property of the network or physical environment or on logical properties of the nodes). The selected set of nodes must also satisfy any provided reference function, a mechanism to scope the location of the query domain to a specified area of interest (e.g., within a certain distance from a specified reference point). In this article, we model these abstractions and present a set of protocols that accomplish this task with varying degrees of correctness. We evaluate their performance through simulation and highlight the tradeoffs between protocol overhead and correctness.

Efficient query processing in sensor networks involves identifying groups of nodes that coordinate to satisfy applications' requests. In this paper, we propose a query domain abstraction that allows an application to dynamically specify the nodes best suited to answering a particular query. To self-organize into such a coalition, nodes must satisfy a proximity function, a user-defined function that constrains the relative relationship among the group of nodes (e.g., based on a property of the network or physical environment or a logical property of the nodes). The proximity function removes the need to explicitly tag nodes with context information, and it provides a convenient mechanism for forming coalitions on-the-fly at query time. This facilitates the deployment of general-purpose sensor networks, where multiple applications can be run in the same network at the same time. In this paper, we model this abstraction, present a protocol to support the abstraction, and evaluate their performance.