arXiv subjects

Primal Pappachan

Publications and source records attributed to Primal Pappachan.

11 recordsLinked to original sources

arXiv2026

Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases

Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. Specifically, Fine-grained Access Control (FGAC) which is required to ensure that data access adheres to user-specific policies is not fully supported in modern vector databases. Unlike relational databases, vector databases combine structured and unstructured attributes to provide semantic, approximate query results, which complicates FGAC implementation. This creates an inherent tension between enforcing FGAC policies correctly, achieving high ANN search recall and maintaining low query latency. In this paper, we present a vision for Policy-aware Vector Search by formalizing the FGAC policy model in vector databases as well as the enforcement problem. We compare various enforcement strategies, present preliminary findings, and identify key open challenges for future research in policy-aware vector search.

cs.DB↗

arXiv2026

Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning

While large language models (LLMs) enable strong question answering (QA), budgeted deployment is complicated by nondeterminism and heterogeneous resource profiles (cost, latency, and energy). We present OPTI-Q, a database-inspired, cost-based optimizer that implements a plan-before-execute paradigm for multi-LLM orchestration. OPTI-Q models LLM invocations as physical operators in an execution DAG and, for each question, searches for plans that optimize answer quality (QoA) while trading off financial cost, latency, and energy under user-specified resource constraints. Plans can include sequential operators that pass intermediate answers as context and parallel/blend operators that run models concurrently and merge their outputs. To search this space without executing each candidate plan, OPTI-Q uses PERFDB, a statistics catalog populated and refreshed from benchmarks and execution traces, to estimate the QoA and resource costs of both individual operators and composed subplans. Using these estimates, OPTI-Q performs Pareto-frontier search and selects a final plan based on user preferences. On MMLU-Pro and SimpleQA under user-specified budgets, OPTI-Q improves average QoA by ~58% and ~41% over baselines at comparable cost, demonstrating that database-style planning yields better quality-resource trade-offs for multi-LLM QA.

cs.AI↗

arXiv2025

Scalable Enforcement of Fine Grained Access Control Policies in Relational Database Management Systems

The proliferation of smart technologies and evolving privacy regulations such as the GDPR and CPRA has increased the need to manage fine-grained access control (FGAC) policies in database management systems (DBMSs). Existing approaches to enforcing FGAC policies do not scale to thousands of policies, leading to degraded query performance and reduced system effectiveness. We present Sieve, a middleware for relational DBMSs that combines query rewriting and caching to optimize FGAC policy enforcement. Sieve rewrites a query with guarded expressions that group and filter policies and can efficiently use indexes in the DBMS. It also integrates a caching mechanism with an effective replacement strategy and a refresh mechanism to adapt to dynamic workloads. Experiments on two DBMSs with real and synthetic datasets show that Sieve scales to large datasets and policy corpora, maintaining low query latency and system load and improving policy evaluation performance by between 2x and 10x on workloads with 200 to 1,200 policies. The caching extension further improves query performance by between 6 and 22 percent under dynamic workloads, especially with larger cache sizes. These results highlight Sieve's applicability for real-time access control in smart environments and its support for efficient, scalable management of user preferences and privacy policies.

cs.DB↗

arXiv2025

Meaningful Data Erasure in the Presence of Dependencies

Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets.

cs.DB↗

arXiv2024

Time-Efficient Locally Relevant Geo-Location Privacy Protection

Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect users' location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geo-obfuscation calculation for each user exclusively to the locally relevant (LR) locations to the user's actual location. Given the potential risk of LR locations disclosing a user's actual whereabouts, we enable users to compute the LP coefficients locally and upload them only to the server, rather than the LR locations. The server then solves the LP problem based on the received coefficients. Furthermore, we refine the LP framework by incorporating an exponential obfuscation mechanism to guarantee the indistinguishability of obfuscation distribution across multiple users. Based on the constraint structure of the LP formulation, we apply Benders' decomposition to further enhance computational efficiency. Our theoretical analysis confirms that, despite the geo-obfuscation being calculated independently for each user, it still meets geo-indistinguishability constraints across multiple users with high probability. Finally, the experimental results based on a real-world dataset demonstrate that LR-Geo outperforms existing geo-obfuscation methods in computational time, data utility, and privacy preservation.

cs.CR↗

arXiv2023

Preventing Inferences through Data Dependencies on Sensitive Data

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage -- resulting in poor utility, or only protects against exact reconstruction of the sensitive data -- resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for efficiently implementing full deniability on a given database instance with a set of data dependencies and sensitive cells. Using experiments on two different datasets, we demonstrate that our approach protects against realistic adversaries while hiding only minimal number of additional non-sensitive cells and scales well with database size and sensitive data.

cs.DB↗

arXiv2023

GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants

Privacy policies of websites are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants that can answer users questions about privacy policies. However, genAIs reliability is a concern due to its potential for producing inaccurate information. This study introduces GenAIPABench, a benchmark for evaluating Generative AI-based Privacy Assistants (GenAIPAs). GenAIPABench includes: 1) A set of questions about privacy policies and data protection regulations, with annotated answers for various organizations and regulations; 2) Metrics to assess the accuracy, relevance, and consistency of responses; and 3) A tool for generating prompts to introduce privacy documents and varied privacy questions to test system robustness. We evaluated three leading genAI systems ChatGPT-4, Bard, and Bing AI using GenAIPABench to gauge their effectiveness as GenAIPAs. Our results demonstrate significant promise in genAI capabilities in the privacy domain while also highlighting challenges in managing complex queries, ensuring consistency, and verifying source accuracy.

cs.CR↗

arXiv2023

A Study of the Landscape of Privacy Policies of Smart Devices

As the adoption of smart devices continues to permeate all aspects of our lives, user privacy concerns have become more pertinent than ever. Privacy policies outline the data handling practices of these devices. Prior work in the domains of websites and mobile apps has shown that privacy policies are rarely read and understood by users. In these domains, automatic analysis of privacy policies has been shown to help give users appropriate insights. However, there is a lack of such an analysis in the domain of smart device privacy policies. This paper presents a comprehensive study of the landscape of privacy policies of smart devices. We introduce a methodology that addresses the unique challenges of smart devices, by finding information about them, their manufacturers, and their privacy policies on the Web. Our methodology utilizes state-of-the-art analysis techniques to assess readability and privacy of smart device policies and compares it policies of e-commerce websites and mobile applications. Overall, we analyzed 4,556 smart devices, 2,211 manufacturers, and 819 privacy policies. Despite smart devices having access to more intrusive data about their users (using sensors such as cameras and microphones), more than 1,167 of the analyzed manufacturers did not have policies available. The study highlights that significant improvement is required on communicating the data management practices of smart devices.

cs.CY↗

arXiv2023

Differentially Private Streaming Data Release under Temporal Correlations via Post-processing

The release of differentially private streaming data has been extensively studied, yet striking a good balance between privacy and utility on temporally correlated data in the stream remains an open problem. Existing works focus on enhancing privacy when applying differential privacy to correlated data, highlighting that differential privacy may suffer from additional privacy leakage under correlations; consequently, a small privacy budget has to be used which worsens the utility. In this work, we propose a post-processing framework to improve the utility of differential privacy data release under temporal correlations. We model the problem as a maximum posterior estimation given the released differentially private data and correlation model and transform it into nonlinear constrained programming. Our experiments on synthetic datasets show that the proposed approach significantly improves the utility and accuracy of differentially private data by nearly a hundred times in terms of mean square error when a strict privacy budget is given.

cs.DB↗

arXiv2022

User Customizable and Robust Geo-Indistinguishability for Location Privacy

Location obfuscation functions generated by existing systems for ensuring location privacy are monolithic and do not allow users to customize their obfuscation range. This can lead to the user being mapped in undesirable locations (e.g., shady neighborhoods) to the location-requesting services. Modifying the obfuscation function generated by a centralized server on the user side can result in poor privacy as the original function is not robust against such updates. Users themselves might find it challenging to understand the parameters involved in obfuscation mechanisms (e.g., obfuscation range and granularity of location representation) and therefore struggle to set realistic trade-offs between privacy, utility, and customization. In this paper, we propose a new framework called, CORGI, i.e., CustOmizable Robust Geo-Indistinguishability, which generates location obfuscation functions that are robust against user customization while providing strong privacy guarantees based on the Geo-Indistinguishability paradigm. CORGI utilizes a tree representation of a given region to assist users in specifying their privacy and customization requirements. The server side of CORGI takes these requirements as inputs and generates an obfuscation function that satisfies Geo-Indistinguishability requirements and is robust against customization on the user side. The obfuscation function is returned to the user who can then choose to update the obfuscation function (e.g., obfuscation range, granularity of location representation). The experimental results on a real dataset demonstrate that CORGI can efficiently generate obfuscation matrices that are more robust to the customization by users.

cs.CR↗

arXiv2020

Sieve: A Middleware Approach to Scalable Access Control for Database Management Systems

Current approaches of enforcing FGAC in Database Management Systems (DBMS) do not scale in scenarios when the number of policies are in the order of thousands. This paper identifies such a use case in the context of emerging smart spaces wherein systems may be required by legislation, such as Europe's GDPR and California's CCPA, to empower users to specify who may have access to their data and for what purposes. We present Sieve, a layered approach of implementing FGAC in existing database systems, that exploits a variety of it's features such as UDFs, index usage hints, query explain; to scale to large number of policies. Given a query, Sieve exploits it's context to filter the policies that need to be checked. Sieve also generates guarded expressions that saves on evaluation cost by grouping the policies and cuts the read cost by exploiting database indices. Our experimental results, on two DBMS and two different datasets, show that Sieve scales to large data sets and to large policy corpus thus supporting real-time access in applications including emerging smart environments.

cs.DB↗