arXiv subjects

Pranav Subramaniam

Publications and source records attributed to Pranav Subramaniam.

4 recordsLinked to original sources

arXiv2024

DePLOI: Applying NL2SQL to Synthesize and Audit Database Access Control

In every enterprise database, administrators must define an access control policy that specifies which users have access to which tables. Access control straddles two worlds: policy (organization-level principles that define who should have access) and process (database-level primitives that actually implement the policy). Assessing and enforcing process compliance with a policy is a manual and ad-hoc task. This paper introduces a new access control model called Intent-Based Access Control for Databases (IBAC-DB). In IBAC-DB, access control policies are expressed using abstractions that scale to high numbers of database objects, and are traceable with respect to implementations. This paper proposes DePLOI (Deployment Policy Linter for Organization Intents), a LLM-backed system leveraging access control-specific task decompositions to accurately synthesize and audit access control implementation from IBAC-DB abstractions. As DePLOI is the first system of its kind to our knowledge, this paper further proposes IBACBench, the first benchmark for evaluating the synthesis and auditing capabilities of DePLOI. IBACBench leverages a combination of current NL2SQL benchmarks, real-world role hierarchies and access control policies, and LLM-generated data. We find that DePLOI achieves high synthesis accuracies and auditing F1 scores overall, and greatly outperforms other LLM prompting strategies (e.g., by 10 F1 points).

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arXiv2023

Kitana: Efficient Data Augmentation Search for AutoML

AutoML services provide a way for non-expert users to benefit from high-quality ML models without worrying about model design and deployment, in exchange for a charge per hour ($21.252 for VertexAI). However, existing AutoML services are model-centric, in that they are limited to extracting features and searching for models from initial training data-they are only as effective as the initial training data quality. With the increasing volume of tabular data available, there is a huge opportunity for data augmentation. For instance, vertical augmentation adds predictive features, while horizontal augmentation adds examples. This augmented training data yields potentially much better AutoML models at a lower cost. However, existing systems either forgo the augmentation opportunities that provide poor models, or apply expensive augmentation searching techniques that drain users' budgets. Kitana is a data-centric AutoML system that also searches for new tabular datasets that can augment the tabular training data with new features and/or examples. Kitana manages a corpus of datasets, exposes an AutoML interface to users and searches for augmentation with datasets in the corpus to improve AutoML performance. To accelerate search, Kitana applies aggressive pre-computation to train a factorized proxy model and evaluate each candidate augmentation within 0.1s. Kitana also uses a cost model to limit the time spent on augmentation search, supports expressive data access controls, and performs request caching to benefit from past similar requests. Using a corpus of 518 open-source datasets, Kitana produces higher quality models than existing AutoML systems in orders of magnitude less time. Across different user requests, Kitana increases the model R2 from 0.16 to 0.66 while reducing the cost by >100x compared to the naive factorized learning and SOTA data augmentation search.

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arXiv2023

Comprehensive and Comprehensible Data Catalogs: The What, Who, Where, When, Why, and How of Metadata Management

Data management tasks require access to metadata, which is increasingly tracked by databases called data catalogs. Current catalogs are too dependent on users' understanding of data, leading to difficulties in large organizations of users with different skills: catalogs either make metadata easy for users to store and difficult to retrieve, or they make it easy to retrieve, but difficult to store. In this paper, we present 5W1H+R, a new catalog mental model that is comprehensive in the metadata it represents, and comprehensible in that it permits all users to locate metadata easily. We demonstrate these properties via a user study. We then discuss practical guidelines for implementing the new mental model. We conclude mental models are important to make data catalogs more useful and to boost metadata management efforts.

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arXiv2020

Data Market Platforms: Trading Data Assets to Solve Data Problems

Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover and integrate is hard because data owners lack information (who needs what data) and they do not have incentives to prepare the data in a way that is easy to consume by others. In this paper, we propose data market platforms to address the lack of information and incentives and tackle the problems of data sharing, discovery, and integration. In a data market platform, data owners want to share data because they will be rewarded if they do so. Consumers are encouraged to share their data needs because the market will solve the discovery and integration problem for them in exchange for some form of currency. We consider internal markets that operate within organizations to bring down data silos, as well as external markets that operate across organizations to increase the value of data for everybody. We outline a research agenda that revolves around two problems. The problem of market design, or how to design rules that lead to the outcomes we want, and the systems problem, how to implement the market and enforce the rules. Treating data as a first-class asset is sorely needed to extend the value of data to more organizations, and we propose data market platforms as one mechanism to achieve this goal.

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