arXiv subjects

Sharad Mehrotra

Publications and source records attributed to Sharad Mehrotra.

At least 37 records · Page 2Linked to original sources

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.

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arXiv2023

Data-CASE: Grounding Data Regulations for Compliant Data Processing Systems

Data regulations, such as GDPR, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are, often ambiguous (with multiple valid interpretations) when it comes to defining the expected dynamic behavior of data processing systems. This paper argues that it is possible to represent regulations such as GDPR formally as invariants using a (small set of) data processing concepts that capture system behavior. When such concepts are grounded, i.e., they are provided with a single unambiguous interpretation, systems can achieve compliance by demonstrating that the system-actions they implement maintain the invariants (representing the regulations). To illustrate our vision, we propose Data-CASE, a simple yet powerful model that (a) captures key data processing concepts (b) a set of invariants that describe regulations in terms of these concepts. We further illustrate the concept of grounding using "deletion" as an example and highlight several ways in which end-users, companies, and software designers/engineers can use Data-CASE.

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arXiv2023

TransEdge: Supporting Efficient Read Queries Across Untrusted Edge Nodes

We propose Transactional Edge (TransEdge), a distributed transaction processing system for untrusted environments such as edge computing systems. What distinguishes TransEdge is its focus on efficient support for read-only transactions. TransEdge allows reading from different partitions consistently using one round in most cases and no more than two rounds in the worst case. TransEdge design is centered around this dependency tracking scheme including the consensus and transaction processing protocols. Our performance evaluation shows that TransEdge's snapshot read-only transactions achieve an 9-24x speedup compared to current byzantine systems.

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arXiv2023

Federated Analytics: A survey

Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.

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arXiv2022

QUIP: Query-driven Missing Value Imputation

Missing values widely exist in real-world data sets, and failure to clean the missing data may result in the poor quality of answers to queries. \yiming{Traditionally, missing value imputation has been studied as an offline process as part of preparing data for analysis.} This paper studies query-time missing value imputation and proposes QUIP, which only imputes minimal missing values to answer the query. Specifically, by taking a reasonable good query plan as input, QUIP tries to minimize the missing value imputation cost and query processing overhead. QUIP proposes a new implementation of outer join to preserve missing values in query processing and a bloom filter based index structure to optimize the space and runtime overhead. QUIP also designs a cost-based decision function to automatically guide each operator to impute missing values now or delay imputations. Efficient optimizations are proposed to speed-up aggregate operations in QUIP, such as MAX/MIN operator. Extensive experiments on both real and synthetic data sets demonstrates the effectiveness and efficiency of QUIP, which outperforms the state-of-the-art ImputeDB by 2 to 10 times on different query sets and data sets, and achieves the order-of-magnitudes improvement over the offline approach.

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arXiv2022

LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization

This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks - first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.

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arXiv2021

Contrained Generalization For Data Anonymization - A Systematic Search Based Approach

Data generalization is a powerful technique for sanitizing multi-attribute data for publication. In a multidimensional model, a subset of attributes called the quasi-identifiers (QI) are used to define the space and a generalization scheme corresponds to a partitioning of the data space. The process of sanitization can be modeled as a constrained optimization problem where the information loss metric is to be minimized while ensuring that the privacy criteria are enforced. The privacy requirements translate into constraints on the partitions (bins), like minimum occupancy constraints for k-anonymity, value diversity constraint for l-diversity etc. Most algorithms proposed till date use some greedy search heuristic to search for a locally optimal generalization scheme. The performance of such algorithms degrade rapidly as the constraints are made more complex and numerous. To address this issue, in this paper we develop a complete enumeration based systematic search framework that searches for the globally optimal generalization scheme amongst all feasible candidates. We employ a novel enumeration technique that eliminates duplicates and develop effective pruning heuristics that cut down the solution space in order to make the search tractable. Our scheme is versatile enough to accommodate multiple constraints and information loss functions satisfying a set of generic properties (that are usually satisfied by most metrics proposed in literature). Additionally, our approach allows the user to specify various stopping criteria and can give a bound on the approximation factor achieved by any candidate solution. Finally, we carry out extensive experimentation whose results illustrate the power of our algorithm and its advantage over other competing approaches.

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arXiv2021

IoT Notary: Attestable Sensor Data Capture in IoT Environments

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced -- IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals' privacy or service integrity. To address such concerns, we propose IoT Notary, a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable `proof-of-integrity,' based on which a verifier can attest that captured sensor data adheres to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California Irvine to provide various real-time location-based services on the campus. We present extensive experiments over realtime WiFi connectivity data to evaluate IoT Notary, and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day's data in less than two seconds even using a resource-limited device.

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arXiv2021

Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databases

This paper proposes Prism, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.

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arXiv2021

Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets

This paper proposes a system, entitled Concealer that allows sharing time-varying spatial data (e.g., as produced by sensors) in encrypted form to an untrusted third-party service provider to provide location-based applications (involving aggregation queries over selected regions over time windows) to users. Concealer exploits carefully selected encryption techniques to use indexes supported by database systems and combines ways to add fake tuples in order to realize an efficient system that protects against leakage based on output-size. Thus, the design of Concealer overcomes two limitations of existing symmetric searchable encryption (SSE) techniques: (i) it avoids the need of specialized data structures that limit usability/practicality of SSE in large scale deployments, and (ii) it avoids information leakages based on the output-size, which may leak data distributions. Experimental results validate the efficiency of the proposed algorithms over a spatial time-series dataset (collected from a smart space) and TPC-H datasets, each of 136 Million rows, the size of which prior approaches have not scaled to.

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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.

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arXiv2020

Panda: Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along with the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We, first, provide a new security definition, entitled partitioned data security for guaranteeing that the joint processing of non-sensitive data (in cleartext) and sensitive data (in encrypted form) does not lead to any leakage. Then, this paper proposes a new secure approach, entitled query binning (QB) that allows secure execution of queries over non-sensitive and sensitive parts of the data. QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. In particular, we propose secure algorithms for selection, range, and join queries to be executed over encrypted sensitive and cleartext non-sensitive datasets. Interestingly, in addition to improving performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.

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arXiv2020

Quest: Practical and Oblivious Mitigation Strategies for COVID-19 using WiFi Datasets

Contact tracing has emerged as one of the main mitigation strategies to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies, e.g., Bluetooth beacons, cellular data records, and smartphone applications. Such solutions are often intrusive, potentially violating individual privacy rights and are often subject to regulations (e.g., GDPR and CCPR) that mandate the need for opt-in policies to gather and use personal information. In this paper, we introduce Quest, a system that empowers organizations to observe individuals and spaces to implement policies for social distancing and contact tracing using WiFi connectivity data in a passive and privacy-preserving manner. The goal is to ensure the safety of employees and occupants at an organization, while protecting the privacy of all parties. Quest incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data); it includes support for accurately identifying users who were in the vicinity of a confirmed patient, and then informing them via opt-in mechanisms. Quest supports a range of privacy-enabled applications to ensure adherence to social distancing, monitor the flow of people through spaces, identify potentially impacted regions, and raise exposure alerts. We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in Quest. We, also, validate the practicality of Quest and evaluate it thoroughly via an actual campus-scale deployment at UC Irvine over a very large dataset of over 50M tuples.

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arXiv2020

Obscure: Information-Theoretically Secure, Oblivious, and Verifiable Aggregation Queries on Secret-Shared Outsourced Data -- Full Version

Despite exciting progress on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop a communication-efficient and information-theoretically secure system, entitled Obscure for aggregation queries with conjunctive or disjunctive predicates, using secret-sharing. Obscure is strongly secure (i.e., secure regardless of the computational-capabilities of an adversary) and prevents the network, as well as, the (adversarial) servers to learn the user's queries, results, or the database. In addition, Obscure provides additional security features, such as hiding access-patterns (i.e., hiding the identity of the tuple satisfying a query) and hiding query-patterns (i.e., hiding which two queries are identical). Also, Obscure does not require any communication between any two servers that store the secret-shared data before/during/after the query execution. Moreover, our techniques deal with the secret-shared data that is outsourced by a single or multiple database owners, as well as, allows a user, which may not be the database owner, to execute the query over secret-shared data. We further develop (non-mandatory) privacy-preserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of Obscure on large datasets, the size of which prior approaches of secret-sharing or multi-party computation systems have not scaled to.

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arXiv2020

Canopy: A Verifiable Privacy-Preserving Token Ring based Communication Protocol for Smart Homes

This paper focuses on the new privacy challenges that arise in smart homes. Specifically, the paper focuses on inferring the user's activities -- which may, in turn, lead to the user's privacy -- via inferences through device activities and network traffic analysis. We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices to prevent inferences from device activities, via device workflow, i.e., inferences from a coordinated sequence of devices' actuation. The solution hides the device activity and corresponding channel activities, and thus, preserve the individual's activities. We also extend our solution to deal with a large number of devices and devices that produce large-sized data by implementing parallel rings. Our experiments also evaluate the performance in terms of communication overheads of the proposed approach and the obtained privacy.

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arXiv2020

IoT Expunge: Implementing Verifiable Retention of IoT Data

The growing deployment of Internet of Things (IoT) systems aims to ease the daily life of end-users by providing several value-added services. However, IoT systems may capture and store sensitive, personal data about individuals in the cloud, thereby jeopardizing user-privacy. Emerging legislation, such as California's CalOPPA and GDPR in Europe, support strong privacy laws to protect an individual's data in the cloud. One such law relates to strict enforcement of data retention policies. This paper proposes a framework, entitled IoT Expunge that allows sensor data providers to store the data in cloud platforms that will ensure enforcement of retention policies. Additionally, the cloud provider produces verifiable proofs of its adherence to the retention policies. Experimental results on a real-world smart building testbed show that IoT Expunge imposes minimal overheads to the user to verify the data against data retention policies.

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arXiv2019

Network2Vec Learning Node Representation Based on Space Mapping in Networks

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.

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arXiv2019

PIQUE: Progressive Integrated QUery Operator with Pay-As-You-Go Enrichment

Big data today in the form of text, images, video, and sensor data needs to be enriched (i.e., annotated with tags) prior to be effectively queried or analyzed. Data enrichment (that, depending upon the application could be compiled code, declarative queries, or expensive machine learning and/or signal processing techniques) often cannot be performed in its entirety as a pre-processing step at the time of data ingestion. Enriching data as a separate offline step after ingestion makes it unavailable for analysis during the period between the ingestion and enrichment. To bridge such a gap, this paper explores a novel approach that supports progressive data enrichment during query processing in order to support interactive exploratory analysis. Our approach is based on integrating an operator, entitled PIQUE, to support a prioritized execution of the enrichment functions during query processing. Query processing with the PIQUE operator significantly outperforms the baselines in terms of rate at which answer quality improves during query processing.

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