Ashwinkumar Venkatanaga Machanavajjhala

Conference Proceedings of the VLDB Endowment · January 1, 2024 We consider the problem of the private release of statistics (like pay roll) where it is critical to preserve the contribution made by a small number of outlying large entities. We propose a privacy formalism, per-record zero concentrated differential priv ... Full text Cite

PreFair: Privately Generating Justifiably Fair Synthetic Data

Conference Proceedings of the VLDB Endowment · January 1, 2023 When a database is protected by Differential Privacy (DP), its us-ability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the syntheti ... Full text Cite

Explaining Differentially Private Query Results With DPXPlain

Conference Proceedings of the VLDB Endowment · January 1, 2023 Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data i ... Full text Cite

Private Proof-of-Stake Blockchains using Differentially-Private Stake Distortion

Conference 32nd Usenix Security Symposium Usenix Security 2023 · January 1, 2023 Safety, liveness, and privacy are three critical properties for any private proof-of-stake (PoS) blockchain. However, prior work (SP’21) has shown that to obtain safety and liveness, a PoS blockchain must in theory forgo privacy. Specifically, to ensure sa ... Cite

IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 11, 2022 In this paper, we consider secure outsourced growing databases (SOGDB) that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view. This allows servers to use only the materialized view for que ... Full text Cite

R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 11, 2022 Answering SPJA queries under differential privacy (DP), including graph pattern counting under node-DP as an important special case, has received considerable attention in recent years. The dual challenge of foreign-key constraints and self-joins is partic ... Full text Cite

DPXPlain: Privately Explaining Aggregate Query Answers

Conference Proceedings of the VLDB Endowment · January 1, 2022 Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in the data. In today’s era of data analysis, however, it poses new challenges f ... Full text Cite

Practical Security and Privacy for Database Systems

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · January 1, 2021 Computing technology has enabled massive digital traces of our personal lives to be collected and stored. These datasets play an important role in numerous real-life applications and research analysis, such as contact tracing for COVID 19, but they contain ... Full text Cite

DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · January 1, 2021 In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow appendonly data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to securely ou ... Full text Cite

Synthesizing Linked Data under Cardinality and Integrity Constraints

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · January 1, 2021 The generation of synthetic data is useful in multiple aspects, from testing applications to benchmarking to privacy preservation. Generating thelinks between relations, subject tocardinality constraints (CCs) andintegrity constraints (ICs) is an important ... Full text Cite

Budget sharing for multi-analyst differential privacy

Conference Proceedings of the VLDB Endowment · January 1, 2021 Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally required to prote ... Full text Cite

Poirot: Private contact summary aggregation: Poster abstract

Conference Sensys 2020 Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems · November 16, 2020 Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensitive an ... Full text Cite

Crypte: Crypto-Assisted Differential Privacy on Untrusted Servers

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 14, 2020 Differential privacy (DP) is currently the de-facto standard for achieving privacy in data analysis, which is typically implemented either in the "central" or "local" model. The local model has been more popular for commercial deployments as it does not re ... Full text Cite

One-sided differential privacy

Conference Proceedings International Conference on Data Engineering · April 1, 2020 We study the problem of privacy-preserving data sharing, wherein only a subset of the records in a database is sensitive, possibly based on predefined privacy policies. Existing solutions, viz, differential privacy (DP), are over-pessimistic as they treat ... Full text Cite

Fair decision making using privacy-protected data

Conference Fat 2020 Proceedings of the 2020 Conference on Fairness Accountability and Transparency · January 27, 2020 Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is a tra ... Full text Cite

APEX: Accuracy-aware differentially private data exploration

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 25, 2019 Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current ... Full text Cite

Permissions plugins as android apps

Conference Mobisys 2019 Proceedings of the 17th Annual International Conference on Mobile Systems Applications and Services · June 12, 2019 The permissions framework for Android is frustratingly inflexible. Once granted a permission, Android will always allow an app to access the resource until the user manually revokes the app’s permission. Prior work has proposed extensible plugin frameworks ... Full text Cite

Privstream: Differentially private event detection on data streams

Conference Codaspy 2019 Proceedings of the 9th ACM Conference on Data and Application Security and Privacy · March 13, 2019 Event monitoring and detection in real-time systems is crucial. Protecting users’ data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong notion of differential privacy to private strea ... Full text Cite

?ktelo: A framework for defining differentially-private computations

Conference SIGMOD Record · March 1, 2019 The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, ?ktelo, for implementing both existing and new privacy algorithms ... Full text Cite

Architecting a differentially private SQL engine

Conference CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research · January 1, 2019 © 2019 Conference on Innovative Data Systems Research (CIDR). All rights reserved. In recent years, differential privacy (DP) has emerged as the state-of-the-art for privately analyzing sensitive data. Despite its wide acceptance in the academic community ... Cite

Privacy Changes Everything

Conference Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics · January 1, 2019 We are storing and querying datasets with the private information of individuals at an unprecedented scale in settings ranging from IoT devices in smart homes to mining enormous collections of click trails for targeted advertising. Here, the privacy of the ... Full text Cite

Architecting a differentially private SQL engine

Conference Cidr 2019 9th Biennial Conference on Innovative Data Systems Research · January 1, 2019 In recent years, differential privacy (DP) has emerged as the state-of-the-art for privately analyzing sensitive data. Despite its wide acceptance in the academic community and much work on differentially private algorithm design, there is surprisingly lit ... Cite

Capacity bounded differential privacy

Conference Advances in Neural Information Processing Systems · January 1, 2019 Differential privacy has emerged as the gold standard for measuring the risk posed by an algorithm's output to the privacy of a single individual in a dataset. It is defined as the worst-case distance between the output distributions of an algorithm that i ... Cite

IoT-Detective: Analyzing IoT data under differential privacy

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · May 27, 2018 The success of emerging IoT applications depends on integrating privacy protections into the IoT infrastructures to guard against privacy risks posed by sensor-based continuous monitoring of individuals and their activities. This demonstration adapts a rec ... Full text Cite

Differentially private hierarchical countofcounts histograms

Conference Proceedings of the VLDB Endowment · January 1, 2018 We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms. Count-of-counts histograms partition the rows of an input table into groups (e.g., group of people in the same house- hold), and for eve ... Full text Cite

Shrinkwrap: Efficient SQL query processing in differentially private data federations

Conference Proceedings of the VLDB Endowment · January 1, 2018 A private data federation is a set of autonomous databases that share a unified query interface offering in-situ evaluation of SQL queries over the union of the sensitive data of its members. Owing to privacy concerns, these systems do not have a trusted d ... Full text Cite

Optimizing error of highdimensional statistical queries under differential privacy

Conference Proceedings of the VLDB Endowment · January 1, 2018 Differentially private algorithms for answering sets of predicate counting queries on a sensitive database have many applications. Organizations that collect individual-level data, such as statistical agencies and medical institutions, use them to safely r ... Full text Cite

PSynDB: Accurate and accessible private data generation

Conference Proceedings of the VLDB Endowment · January 1, 2018 Across many application domains, trusted parties who collect sensitive information need mechanisms to safely disseminate data. A favored approach is to generate synthetic data: a dataset similar to the original, hopefully retaining its statistical features ... Full text Cite

PrivateSQL: A differentially private SQL query engine

Conference Proceedings of the VLDB Endowment · January 1, 2018 Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific typ ... Full text Cite

PeGaSus: Data-Adaptive differentially private stream processing

Conference Proceedings of the ACM Conference on Computer and Communications Security · October 30, 2017 Individuals are continually observed by an ever-increasing number of sensors that make up the Internet of Things. The resulting streams of data, which are analyzed in real time, can reveal sensitive personal information about individuals. Hence, there is a ... Full text Cite

Composing Differential Privacy and Secure Computation: A case study on scaling private record linkage

Conference Proceedings of the ACM Conference on Computer and Communications Security · October 30, 2017 Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: ( ... Full text Cite

EPrivateeye: To the edge and beyond!

Conference 2017 2nd ACM IEEE Symposium on Edge Computing Sec 2017 · October 12, 2017 Edge computing offers resource-constrained devices lowlatency access to high-performance computing infrastructure. In this paper, we present ePrivateEye, an implementation of PrivateEye that offloads computationally expensive computervision processing to a ... Full text Cite

Protecting Visual Secrets Using Adversarial Nets

Conference IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · August 22, 2017 Protecting visual secrets is an important problem due to the prevalence of cameras that continuously monitor our surroundings. Any viable solution to this problem should also minimize the impact on the utility of applications that use images. In this work, ... Full text Cite

Pythia: Data dependent differentially private algorithm selection

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · May 9, 2017 Differential privacy has emerged as a preferred standard for ensuring privacy in analysis tasks on sensitive datasets. Recent algorithms have allowed for significantly lower error by adapting to properties of the input data. These so-called data-dependent ... Full text Cite

Differential privacy in the wild: A tutorial on current practices & open challenges

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · May 9, 2017 Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security ... Full text Cite

Utility cost of formal privacy for releasing national employer-employee statistics

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · May 9, 2017 National statistical agencies around the world publish tabular summaries based on combined employer-employee (ER-EE) data. The privacy of both individuals and business establishments that feature in these data are protected by law in most countries. These ... Full text Cite

DIAS: Differentially private interactive algorithm selection using Pythia

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · May 9, 2017 Differential privacy has emerged as the dominant privacy standard for data analysis. Its wide acceptance has led to significant development of algorithms that meet this rigorous standard. For some tasks, such as the task of answering low dimensional counti ... Full text Cite

Directed edge recommender system

Conference Wsdm 2017 Proceedings of the 10th ACM International Conference on Web Search and Data Mining · February 2, 2017 Recommender systems have become ubiquitous in online ap- plications where companies personalize the user experience based on explicit or inferred user preferences. Most mod- ern recommender systems concentrate on finding relevant items for each individual ... Full text Cite

Preface

Conference Journal of Ambient Intelligence and Smart Environments · January 1, 2017 Full text Cite

Ayumu: Efficient lifelogging with focused tasks

Conference Mobicase 2016 8th Eai International Conference on Mobile Computing Applications and Services · December 1, 2016 Today’s lifelogging devices capture images periodically without considering what data is important to users. Due to their small form factors and limited battery capacities, these lifeloggers are bound to miss important data either because they record at a ... Full text Cite

Differentially private regression diagnostics

Conference Proceedings IEEE International Conference on Data Mining Icdm · July 2, 2016 Linear and logistic regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the regression model, estimate its parameters, and use the results for inference orprediction. ... Full text Cite

Principled evaluation of differentially private algorithms using DPBENCH

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 26, 2016 Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms that meet this standard. Algorithms are becoming increasingly complex, and in ... Full text Cite

Exploring privacy-accuracy tradeoffs using DPComp

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 26, 2016 The emergence of differential privacy as a primary standard for privacy protection has led to the development, by the research community, of hundreds of algorithms for various data analysis tasks. Yet deployment of these techniques has been slowed by the c ... Full text Cite

What you mark is what apps see

Conference Mobisys 2016 Proceedings of the 14th Annual International Conference on Mobile Systems Applications and Services · June 20, 2016 Users are increasingly vulnerable to inadvertently leaking sensitive information through cameras. In this paper, we investigate an approach to mitigating the risk of such inadvertent leaks called privacy markers. Privacy markers give users fine-grained con ... Full text Cite

A demonstration of VisDPT: Visual exploration of differentially private trajectories

Conference Proceedings of the VLDB Endowment · January 1, 2015 The release of detailed taxi trips has motivated numerous useful studies, but has also triggered multiple privacy attacks on individuals' trips. Despite these attacks, no tools are available for systematically analyzing the privacy risk of released traject ... Full text Cite

Differential privacy in the wild: A tutorial on current practices and open challenges

Conference Proceedings of the VLDB Endowment · January 1, 2015 Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security ... Full text Cite

Scalable Social Coordination with Group Constraints using Enmeshed Queries

Conference CIDR 2013 - 6th Biennial Conference on Innovative Data Systems Research · January 1, 2013 © 2013 Conference on Innovative Data Systems Research (CIDR). All rights reserved. While specific forms of social coordination appear in tools such as Meetup and in game platforms such as XBox LIVE, we introduce a more general model using what we call enme ... Cite

Scalable Social Coordination with Group Constraints using Enmeshed Queries

Conference Cidr 2013 6th Biennial Conference on Innovative Data Systems Research · January 1, 2013 While specific forms of social coordination appear in tools such as Meetup and in game platforms such as XBox LIVE, we introduce a more general model using what we call enmeshed queries. An enmeshed query allows users to declaratively specify an intent to ... Cite

Trusted CVS

Conference Icdew 2006 Proceedings of the 22nd International Conference on Data Engineering Workshops · January 1, 2006 The CVS (Concurrent Versions System) software is a popular method for recording modifications to data objects, in addition to concurrent access to data in a multi-user environment. In current implementations, all users have to trust that the CVS server per ... Full text Cite

A storage and indexing framework for P2P systems

Conference Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers and Posters Www Alt 2004 · May 19, 2004 We present a modularized storage and indexing framework that cleanly separates the functional components of a P2P system, enabling us to tailor the P2P infrastructure to the specific needs of various Internet applications. ... Full text Cite