Cornell Census-NSF Research Node (NCRN)

Publications by grant

1. An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

   John M. Abowd and Ian M. Schmutte

   American Economic Review, forthcoming
2. Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the US Statistical System?

   Daniel H. Weinberg, John M. Abowd, Robert F. Belli, and 13 more authors

   Journal of Survey Statistics and Methodology, 2019

   First published December 2018

   Abs DOI URL OA? Scholar (8) Dimensions (3)

   Abstract. The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodolo

3. Disclosure Limitation and Confidentiality Protection in Linked Data

   John M. Abowd, Ian M. Schmutte, and Lars Vilhuber

   Labor Dynamics Institute, Cornell University, Document 47, Jan 2018

   Abs URL Scholar (7)

   Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.

4. Sorting Between and Within Industries: A Testable Model of Assortative Matching

   John M. Abowd, Francis Kramarz, Sebastien Perez-Duarte, and 1 more author

   Annals of Economics and Statistics, 2018

   Abs DOI URL OA? Dimensions (8)

   We test Shimer’s (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting-more productive workers are employed in more productive industries. The evidence confirms that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.

5. Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data

   John M. Abowd, Kevin L. Mckinney, and Nellie Zhao

   Journal of Labor Economics, 2018

   Abs DOI URL OA? Dimensions (42)

   Using earnings data from the U.S. Census Bureau, this paper analyzes the role of the employer in explaining the rise in earnings inequality in the United States. We first establish a consistent frame of analysis appropriate for administrative data used to study earnings inequality. We show that the trends in earnings inequality in the administrative data from the Longitudinal Employer-Household Dynamics Program are inconsistent with other data sources when we do not correct for the presence of misused SSNs. After this correction to the worker frame, we analyze how the earnings distribution has changed in the last decade. We present a decomposition of the year-to-year changes in the earnings distribution from 2004-2013. Even when simplifying these flows to movements between the bottom 20%, the middle 60% and the top 20% of the earnings distribution, about 20.5 million workers undergo a transition each year. Another 19.9 million move between employment and nonemployment. To understand the role of the firm in these transitions, we estimate a model for log earnings with additive fixed worker and firm effects using all jobs held by eligible workers from 2004-2013. We construct a composite log earnings firm component across all jobs for a worker in a given year and a non-firm component. We also construct a skill-type index. We show that, while the difference between working at a low- or middle-paying firm are relatively small, the gains from working at a top-paying firm are large. Specifically, the benefits of working for a high-paying firm are not only realized today, through higher earnings paid to the worker, but also persist through an increase in the probability of upward mobility. High-paying firms facilitate moving workers to the top of the earnings distribution and keeping them there.

6. An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

   John M. Abowd and Ian M. Schmutte

   Center for Economic Studies, U.S. Census Bureau, Working Papers 18-35, Aug 2018

   Abs DOI URL OA? Dimensions (0)

   Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.

7. Codebook for the SIPP Synthetic Beta v7 [Online]

   Lori B. Reeder, Jordan C. Stanley, and Lars Vilhuber

   Cornell Institute for Social and Economic Research and Labor Dynamics Institute. Cornell University, DDI-C document, 2018

   URL
8. Codebook for the SIPP Synthetic Beta 7.0 (PDF version)

   Lori B. Reeder, Jordan C. Stanley, and Lars Vilhuber

   Cornell Institute for Social and Economic Research and Labor Dynamics Institute. Cornell University, Codebook V20181102b-pdf, Nov 2018

   DOI URL OA? Dimensions (0)
9. Codebook for the SIPP Synthetic Beta 7.0 (DDI-C and PDF)

   Lori B. Reeder, Jordan C. Stanley, and Lars Vilhuber

   Nov 2018

   DOI URL OA? Dimensions (0)
10. Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files.

    Andrew S. Green, Mark J. Kutzbach, and and Lars Vilhuber

    U.S. Census Bureau Center for Economic Studies Discussion, Paper, 2017

    URL Scholar (9)
11. Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics

    Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, and 3 more authors

    In Proceedings of the 2017 International Conference on Management of Data, 2017

    Abs DOI URL Scholar (100) Dimensions (52)

    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 data are currently released using a variety of statistical disclosure limitation (SDL) techniques that do not reveal the exact characteristics of particular employers and employees, but lack provable privacy guarantees limiting inferential disclosures. In this work, we present novel algorithms for releasing tabular summaries of linked ER-EE data with formal, provable guarantees of privacy. We show that state-of-the-art differentially private algorithms add too much noise for the output to be useful. Instead, we identify the privacy requirements mandated by current interpretations of the relevant laws, and formalize them using the Pufferfish framework. We then develop new privacy definitions that are customized to ER-EE data and satisfy the statutory privacy requirements. We implement the experiments in this paper on production data gathered by the U.S. Census Bureau. An empirical evaluation of utility for these data shows that for reasonable values of the privacy-loss parameter {}epsilon}geq 1, the additive error introduced by our provably private algorithms is comparable, and in some cases better, than the error introduced by existing SDL techniques that have no provable privacy guarantees. For some complex queries currently published, however, our algorithms do not have utility comparable to the existing traditional SDL algorithms. Those queries are fodder for future research.

12. Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics

    Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, and 3 more authors

    Labor Dynamics Institute, Cornell University, Document 36, 2017

    Abs URL Scholar (100)

    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 data are currently released using a variety of statistical disclosure limitation (SDL) techniques that do not reveal the exact characteristics of particular employers and employees, but lack provable privacy guarantees limiting inferential disclosures. In this work, we present novel algorithms for releasing tabular summaries of linked ER-EE data with formal, provable guarantees of privacy. We show that state-of-the-art differentially private algorithms add too much noise for the output to be useful. Instead, we identify the privacy requirements mandated by current interpretations of the relevant laws, and formalize them using the Pufferfish framework. We then develop new privacy definitions that are customized to ER-EE data and satisfy the statutory privacy requirements. We implement the experiments in this paper on production data gathered by the U.S. Census Bureau. An empirical evaluation of utility for these data shows that for reasonable values of the privacy-loss parameter {}epsilon}geq 1, the additive error introduced by our provably private algorithms is comparable, and in some cases better, than the error introduced by existing SDL techniques that have no provable privacy guarantees. For some complex queries currently published, however, our algorithms do not have utility comparable to the existing traditional SDL algorithms. Those queries are fodder for future research.

13. Effects of a government-academic partnership: Has the NSF-census bureau research network helped improve the U.S. statistical system?

    Daniel H. Weinberg, John M. Abowd, Robert F. Belli, and 13 more authors

    Center for Economic Studies, U.S. Census Bureau, Working Papers 17-59r, Jan 2017

    Abs URL

    The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN’s research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives.

14. Total error and variability measures with integrated disclosure limitation for quarterly workforce indicators and LEHD origin destination employment statistics in on the map

    Kevin L. McKinney, Andrew S. Green, Lars Vilhuber, and 1 more author

    Center for Economic Studies, U.S. Census Bureau, Working Papers 17-71, Jan 2017

    Abs URL

    We report results from the rst comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI): total employment, beginning-of-quarter employment, full-quarter employment, total payroll, and average monthly earnings of full-quarter employees. Beginning-of-quarter employment is also the main tabulation variable in the LEHD Origin-Destination Employment Statistics (LODES) workplace reports as displayed in OnTheMap (OTM). The evaluation is conducted by generating multiple threads of the edit and imputation models used in the LEHD Infrastructure File System. These threads conform to the Rubin (1987) multiple imputation model, with each thread or implicate being the output of formal probability models that address coverage, edit, and imputation errors. Design-based sampling variability and nite population corrections are also included in the evaluation. We derive special formulas for the Rubin total variability and its components that are consistent with the disclosure avoidance system used for QWI and LODES/OTM workplace reports. These formulas allow us to publish the complete set of detailed total quality measures for QWI and LODES. The analysis reveals that the five publication variables under study are estimated very accurately for tabulations involving at least 10 jobs. Tabulations involving three to nine jobs have quality in the range generally deemed acceptable. Tabulations involving zero, one or two jobs, which are generally suppressed in the QWI and synthesized in LODES, have substantial total variability but their publication in LODES allows the formation of larger custom aggregations, which will in general have the accuracy estimated for tabulations in the QWI based on a similar number of workers.

15. Making Confidential Data Part of Reproducible Research

    Lars Vilhuber and Carl Lagoze

    Labor Dynamics Institute, Cornell University, Document 41, 2017

    URL Scholar (1)
16. Proceedings from the 2017 Cornell-Census-NSF-Sloan Workshop on Practical Privacy

    Lars Vilhuber and Ian Schmutte

    Labor Dynamics Institute, Cornell University, Document 43, 2017

    Abs URL Scholar (0)

    These proceedings report on a workshop hosted at the U.S. Census Bureau on May 8, 2017. Our purpose was to gather experts from various backgrounds together to continue discussing the development of formal privacy systems for Census Bureau data products. This workshop was a successor to a previous workshop held in October 2016 (Vilhuber & Schmutte 2017). At our prior workshop, we hosted computer scientists, survey statisticians, and economists, all of whom were experts in data privacy. At that time we discussed the practical implementation of cutting-edge methods for publishing data with formal, provable privacy guarantees, with a focus on applications to Census Bureau data products. The teams developing those applications were just starting out when our first workshop took place, and we spent our time brainstorming solutions to the various problems researchers were encountering, or anticipated encountering. For these cutting-edge formal privacy models, there had been very little effort in the academic literature to apply those methods in real-world settings with large, messy data. We therefore brought together an expanded group of specialists from academia and government who could shed light on technical challenges, subject matter challenges and address how data users might react to changes in data availability and publishing standards. In May 2017, we organized a follow-up workshop, which these proceedings report on. We reviewed progress made in four different areas. The four topics discussed as part of the workshop were 1. the 2020 Decennial Census; 2. the American Community Survey (ACS); 3. the 2017 Economic Census; 4. measuring the demand for privacy and for data quality. As in our earlier workshop, our goals were to 1. Discuss the specific challenges that have arisen in ongoing efforts to apply formal privacy models to Census data products by drawing together expertise of academic and governmental researchers; 2. Produce short written memos that summarize concrete suggestions for practical applications to specific Census Bureau priority areas.

17. Proceedings from the Synthetic LBD International Seminar

    Lars Vilhuber, Saki Kinney, and Ian Schmutte

    Labor Dynamics Institute, Cornell University, Document 44, 2017

    Abs URL Scholar (0)

    On May 9, 2017, we hosted a seminar to discuss the conditions necessary to implement the SynLBD approach with interested parties, with the goal of providing a straightforward toolkit to implement the same procedure on other data. The proceedings summarize the discussions during the workshop.

18. Proceedings from the 2016 NSF-Sloan Workshop on Practical Privacy

    Lars Vilhuber and Ian Schmutte

    Labor Dynamics Institute, Cornell University, Document 33, 2017

    URL Scholar (0)
19. How Will Statistical Agencies Operate When All Data Are Private?

    John M. Abowd

    Journal of Privacy and Confidentiality, 2017

    Abs DOI URL OA? Dimensions (14)

    The dual problems of respecting citizen privacy and protecting the confidentiality of their data have become hopelessly conflated in the “Big Data” era. There are orders of magnitude more data outside an agency?s firewall than inside it-compromising the integrity of traditional statistical disclosure limitation methods. And increasingly the information processed by the agency was “asked” in a context wholly outside the agency’s operations-blurring the distinction between what was asked and what is published. Already, private businesses like Microsoft, Google and Apple recognize that cybersecurity (safeguarding the integrity and access controls for internal data) and privacy protection (ensuring that what is published does not reveal too much about any person or business) are two sides of the same coin. This is a paradigm-shifting moment for statistical agencies.

20. Sorting Between and Within Industries: A Testable Model of Assortative Matching

    John M. Abowd, Francis Kramarz, Sebastien Perez-Duarte, and 1 more author

    Labor Dynamics Institute, Document 40, 2017

    Abs URL

    We test Shimer’s (2005) theory of the sorting of workers between and within industrial sectors based on directed search with coordination frictions, deliberately maintaining its static general equilibrium framework. We fit the model to sector-specific wage, vacancy and output data, including publicly-available statistics that characterize the distribution of worker and employer wage heterogeneity across sectors. Our empirical method is general and can be applied to a broad class of assignment models. The results indicate that industries are the loci of sorting–more productive workers are employed in more productive industries. The evidence confirms that strong assortative matching can be present even when worker and employer components of wage heterogeneity are weakly correlated.

21. Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics

    Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, and 2 more authors

    Proceedings of the 2017 ACM International Conference on Management of Data, 2017

    Abs DOI URL OA? Dimensions (52)

    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 data are currently released using a variety of statistical disclosure limitation (SDL) techniques that do not reveal the exact characteristics of particular employers and employees, but lack provable privacy guarantees limiting inferential disclosures. In this work, we present novel algorithms for releasing tabular summaries of linked ER-EE data with formal, provable guarantees of privacy. We show that state-of-the-art differentially private algorithms add too much noise for the output to be useful. Instead, we identify the privacy requirements mandated by current interpretations of the relevant laws, and formalize them using the Pufferfish framework. We then develop new privacy definitions that are customized to ER-EE data and satisfy the statutory privacy requirements. We implement the experiments in this paper on production data gathered by the U.S. Census Bureau. An empirical evaluation of utility for these data shows that for reasonable values of the privacy-loss parameter ε>= 1, the additive error introduced by our provably private algorithms is comparable, and in some cases better, than the error introduced by existing SDL techniques that have no provable privacy guarantees. For some complex queries currently published, however, our algorithms do not have utility comparable to the existing traditional SDL algorithms. Those queries are fodder for future research.

22. Using partially synthetic microdata to protect sensitive cells in business statistics

    Javier Miranda and Lars Vilhuber

    Statistical Journal of the International Association for Official Statistics, 2016

    Abs DOI URL OA? Scholar (10) Dimensions (4)

    We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive cells. We describe different algorithms, and present preliminary results when applied to the Census Bureau’s Business Dynamics Statistics and Synthetic Longitudinal Business Database, highlighting accuracy and protection afforded by the method when compared to existing public-use tabulations (with suppressions).

23. Synthetic establishment microdata around the world

    Lars Vilhuber, John M. Abowd, and Jerome P. Reiter

    Statistical Journal of the International Association for Official Statistics, 2016

    Abs DOI OA? Scholar (19) Dimensions (11)

    In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large part, this difficulty in providing access to business micro data is due to the skewed and sparse distributions that characterize business data. Synthetic data are simulated data generated from statistical models. We organized sessions at the 2015 World Statistical Congress and the 2015 Joint Statistical Meetings, highlighting work on synthetic establishment microdata. This overview situates those papers, published in this issue, within the broader literature.

24. Noise infusion as a confidentiality protection measure for graph-based statistics

    John M. Abowd and Kevin L. McKinney

    Statistical Journal of the IAOS, Feb 2016

    Abs DOI URL OA? Dimensions (3)

    We use the bipartite graph representation of longitudinally linked employer-employee data, and the associated projections onto the employer and employee nodes, respectively, to characterize the set of potential statistical summaries that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightforward extension of the dynamic noise-infusion method used in the U.S. Census Bureau’s Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs.

25. Modeling Endogenous Mobility in Wage Determination

    John M. Abowd, Kevin L. McKinney, and Ian M. Schmutte

    Labor Dynamics Institute, Document 28, May 2016

    Abs URL

    We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.

26. How Will Statistical Agencies Operate When All Data Are Private?

    John M. Abowd

    Labor Dynamics Institute, Cornell University, Document 30, 2016

    Abs

    The dual problems of respecting citizen privacy and protecting the confidentiality of their data have become hopelessly conflated in the “Big Data” era. There are orders of magnitude more data outside an agency?s firewall than inside it-compromising the integrity of traditional statistical disclosure limitation methods. And increasingly the information processed by the agency was “asked” in a context wholly outside the agency’s operations-blurring the distinction between what was asked and what is published. Already, private businesses like Microsoft, Google and Apple recognize that cybersecurity (safeguarding the integrity and access controls for internal data) and privacy protection (ensuring that what is published does not reveal too much about any person or business) are two sides of the same coin. This is a paradigm-shifting moment for statistical agencies.

27. Why Statistical Agencies Need to Take Privacy-loss Budgets Seriously, and What It Means When They Do

    John M. Abowd

    Labor Dynamics Institute, Cornell University, Document 32, 2016

    Abs

    To appear on fcsm.sites.usa.gov, as presented to the 2016 FCSM Statistical Policy Seminar.

28. Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods

    John M. Abowd and Ian Schmutte

    Labor Dynamics Institute, Document 22, Jan 2015

    Abs URL

    We consider the problem of the public release of statistical information about a population?explicitly accounting for the public-good properties of both data accuracy and privacy loss. We first consider the implications of adding the public-good component to recently published models of private data publication under differential privacy guarantees using a Vickery-Clark-Groves mechanism and a Lindahl mechanism. We show that data quality will be inefficiently under-supplied. Next, we develop a standard social planner?s problem using the technology set implied by (?, ?)-differential privacy with (?, ?)-accuracy for the Private Multiplicative Weights query release mechanism to study the properties of optimal provision of data accuracy and privacy loss when both are public goods. Using the production possibilities frontier implied by this technology, explicitly parameterized interdependent preferences, and the social welfare function, we display properties of the solution to the social planner?s problem. Our results directly quantify the optimal choice of data accuracy and privacy loss as functions of the technology and preference parameters. Some of these properties can be quantified using population statistics on marginal preferences and correlations between income, data accuracy preferences, and privacy loss preferences that are available from survey data. Our results show that government data custodians should publish more accurate statistics with weaker privacy guarantees than would occur with purely private data publishing. Our statistical results using the General Social Survey and the Cornell National Social Survey indicate that the welfare losses from under-providing data accuracy while over-providing privacy protection can be substantial.

29. Codebook for the SIPP Synthetic Beta v6.0.2 [Online]

    Lori B. Reeder, Martha Stinson, Kelly E. Trageser, and 1 more author

    Cornell Institute for Social and Economic Research and Labor Dynamics Institute. Cornell University, DDI-C document, 2015

    URL
30. CED²AR: The Comprehensive Extensible Data Documentation and Access Repository

    Carl Lagoze, Lars Vilhuber, Jeremy Williams, and 2 more authors

    In ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014), Sep 2014

    Presented at the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014)

    Abs DOI URL OA? Dimensions (2)

    Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfill US federal data management mandates and interfering with basic scholarly practices such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access, and cite that data.

31. Graph Kernels via Functional Embedding

    Anshumali Shrivastava and Ping Li

    CoRR, 2014

    Abs URL

    We propose a representation of graph as a functional object derived from the power iteration of the underlying adjacency matrix. The proposed functional representation is a graph invariant, i.e., the functional remains unchanged under any reordering of the vertices. This property eliminates the difficulty of handling exponentially many isomorphic forms. Bhattacharyya kernel constructed between these functionals significantly outperforms the state-of-the-art graph kernels on 3 out of the 4 standard benchmark graph classification datasets, demonstrating the superiority of our approach. The proposed methodology is simple and runs in time linear in the number of edges, which makes our kernel more efficient and scalable compared to many widely adopted graph kernels with running time cubic in the number of vertices.

32. In Defense of MinHash Over SimHash

    Anshumali Shrivastava and Ping Li

    In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014

    Abs URL

    MinHash and SimHash are the two widely adopted Locality Sensitive Hashing (LSH) algorithms for large-scale data processing applications. Deciding which LSH to use for a particular problem at hand is an important question, which has no clear answer in the existing literature. In this study, we provide a theoretical answer (validated by experiments) that MinHash virtually always outperforms SimHash when the data are binary, as common in practice such as search. The collision probability of MinHash is a function of resemblance similarity (R), while the collision probability of SimHash is a function of cosine similarity (S). To provide a common basis for comparison, we evaluate retrieval results in terms of S for both MinHash and SimHash. This evaluation is valid as we can prove that MinHash is a valid LSH with respect to S, by using a general inequality S2≤R≤S2−S. Our worst case analysis can show that MinHash significantly outperforms SimHash in high similarity region. Interestingly, our intensive experiments reveal that MinHash is also substantially better than SimHash even in datasets where most of the data points are not too similar to each other. This is partly because, in practical data, often R≥Sz−S holds where z is only slightly larger than 2 (e.g., z≤2.1). Our restricted worst case analysis by assuming Sz−S≤R≤S2−S shows that MinHash indeed significantly outperforms SimHash even in low similarity region. We believe the results in this paper will provide valuable guidelines for search in practice, especially when the data are sparse.

33. Encoding Provenance Metadata for Social Science Datasets.

    Carl Lagoze, Jeremy Willliams, and Lars Vilhuber

    In Metadata and Semantics Research. Communications in Computer and Information Science, 2013

    DOI OA? Scholar (10) Dimensions (7)
34. Data Management of Confidential Data

    Carl Lagoze, William C. Block, Jeremy Williams, and 2 more authors

    International Journal of Digital Curation, 2013

    DOI Scholar (22) Dimensions (9)
35. Encoding Provenance of Social Science Data: Integrating PROV with DDI

    Carl Lagoze, William C. Block, Jeremy Williams, and 1 more author

    In 5th Annual European DDI User Conference, 2013

    Abs DOI URL OA? Dimensions (0)

    Provenance is a key component of evaluating the integrity and reusability of data for scholarship. While recording and providing access provenance has always been important, it is even more critical in the web environment in which data from distributed sources and of varying integrity can be combined and derived. The PROV model, developed under the auspices of the W3C, is a foundation for semantically-rich, interoperable, and web-compatible provenance metadata. We report on the results of our experimentation with integrating the PROV model into the DDI metadata for a complex, but characteristic, example social science data. We also present some preliminary thinking on how to visualize those graphs in the user interface.

36. b-Bit Minwise Hashing in Practice

    Ping Li, Anshumali Shrivastava, and Arnd Christian König

    In Internetware 2013, Oct 2013

    Abs URL

    Minwise hashing is a standard technique in the context of search for approximating set similarities. The recent work [26, 32] demonstrated a potential use of b-bit minwise hashing [23, 24] for efficient search and learning on massive, high-dimensional, binary data (which are typical for many applications in Web search and text mining). In this paper, we focus on a number of critical issues which must be addressed before one can apply b-bit minwise hashing to the volumes of data often used industrial applications. Minwise hashing requires an expensive preprocessing step that computes k (e.g., 500) minimal values after applying the corresponding permutations for each data vector. We developed a parallelization scheme using GPUs and observed that the preprocessing time can be reduced by a factor of 20   80 and becomes substantially smaller than the data loading time. Reducing the preprocessing time is highly beneficial in practice, e.g., for duplicate Web page detection (where minwise hashing is a major step in the crawling pipeline) or for increasing the testing speed of online classifiers. Another critical issue is that for very large data sets it becomes impossible to store a (fully) random permutation matrix, due to its space requirements. Our paper is the first study to demonstrate that b-bit minwise hashing implemented using simple hash functions, e.g., the 2-universal (2U) and 4-universal (4U) hash families, can produce very similar learning results as using fully random permutations. Experiments on datasets of up to 200GB are presented.

37. Exact Sparse Recovery with L0 Projections

    Ping Li and Cun-Hui Zhang

    In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, 2013

    Abs DOI URL OA? Dimensions (1)

    Many applications (e.g., anomaly detection) concern sparse signals. This paper focuses on the problem of recovering a K-sparse signal x ∈ R/1×N, i.e., K << N and ∑N/i=1 1xi ≠ 0 = K. In the mainstream framework of compressed sensing (CS), × is recovered from M linear measurements y = xS ∈ R/1×M, where S ∈ RN×M is often a Gaussian (or Gaussian-like) design matrix. In our proposed method, the design matrix S is generated from an α-stable distribution with α ≈ 0. Our decoding algorithm mainly requires one linear scan of the coordinates, followed by a few iterations on a small number of coordinates which are "undetermined" in the previous iteration. Our practical algorithm consists of two estimators. In the first iteration, the (absolute) minimum estimator is able to filter out a majority of the zero coordinates. The gap estimator, which is applied in each iteration, can accurately recover the magnitudes of the nonzero coordinates. Comparisons with linear programming (LP) and orthogonal matching pursuit (OMP) demonstrate that our algorithm can be significantly faster in decoding speed and more accurate in recovery quality, for the task of exact spare recovery. Our procedure is robust against measurement noise. Even when there are no sufficient measurements, our algorithm can still reliably recover a significant portion of the nonzero coordinates.

38. Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search

    Anshumali Shrivastava and Ping Li

    In Advances in Neural Information Processing Systems 26, 2013

    Abs URL

    We go beyond the notion of pairwise similarity and look into search problems with k-way similarity functions. In this paper, we focus on problems related to 3-way Jaccard similarity: R3way = |S1∩S2∩S3| |S1∪S2∪S3| , S1, S2, S3 ∈ C, where C is a size n collection of sets (or binary vectors). We show that approximate R3way similarity search problems admit fast algorithms with provable guarantees, analogous to the pairwise case. Our analysis and speedup guarantees naturally extend to k-way resemblance. In the process, we extend traditional framework of locality sensitive hashing (LSH) to handle higher-order similarities, which could be of independent theoretical interest. The applicability of R3way search is shown on the “Google Sets” application. In addition, we demonstrate the advantage of R3way resemblance over the pairwise case in improving retrieval quality.

39. A Proposed Solution to the Archiving and Curation of Confidential Scientific Inputs

    J.M. Abowd, Lars Vilhuber, and William C. Block

    In Privacy in Statistical Databases, 2012

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    We develop the core of a method for solving the data archive and curation problem that confronts the custodians of restricted-access research data and the scientific users of such data. Our solution recognizes the dual protections afforded by physical security and access limitation protocols. It is based on extensible tools and can be easily incorporated into existing instructional materials.

40. One Permutation Hashing

    Ping Li, Art Owen, and Cun-Hui Zhang

    In Advances in Neural Information Processing Systems 25, 2012

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    While minwise hashing is promising for large-scale learning in massive binary data, the preprocessing cost is prohibitive as it requires applying (e.g.,) k=500 permutations on the data. The testing time is also expensive if a new data point (e.g., a new document or a new image) has not been processed. In this paper, we develop a simple \textbfone permutation hashing scheme to address this important issue. While it is true that the preprocessing step can be parallelized, it comes at the cost of additional hardware and implementation. Also, reducing k permutations to just one would be much more \textbfenergy-efficient, which might be an important perspective as minwise hashing is commonly deployed in the search industry. While the theoretical probability analysis is interesting, our experiments on similarity estimation and SVM & logistic regression also confirm the theoretical results.

41. GPU-based minwise hashing: GPU-based minwise hashing

    Ping Li, Anshumali Shrivastava, and Arnd Christian König

    In Proceedings of the 21st World Wide Web Conference (WWW 2012) (Companion Volume), 2012

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    Minwise hashing is a standard technique for efficient set similarity estimation in the context of search. The recent work of b-bit minwise hashing provided a substantial improvement by storing only the lowest b bits of each hashed value. Both minwise hashing and b-bit minwise hashing require an expensive preprocessing step for applying k (e.g., k=500) permutations on the entire data in order to compute k minimal values as the hashed data. In this paper, we developed a parallelization scheme using GPUs, which reduced the processing time by a factor of 20-80. Reducing the preprocessing time is highly beneficial in practice, for example, for duplicate web page detection (where minwise hashing is a major step in the crawling pipeline) or for increasing the testing speed of online classifiers (when the test data are not preprocessed).

42. Entropy Estimations Using Correlated Symmetric Stable Random Projections

    Ping Li and Cun-Hui Zhang

    In Advances in Neural Information Processing Systems 25, 2012

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    Methods for efficiently estimating the Shannon entropy of data streams have important applications in learning, data mining, and network anomaly detections (e.g., the DDoS attacks). For nonnegative data streams, the method of Compressed Counting (CC) based on maximally-skewed stable random projections can provide accurate estimates of the Shannon entropy using small storage. However, CC is no longer applicable when entries of data streams can be below zero, which is a common scenario when comparing two streams. In this paper, we propose an algorithm for entropy estimation in general data streams which allow negative entries. In our method, the Shannon entropy is approximated by the finite difference of two correlated frequency moments estimated from correlated samples of symmetric stable random variables. Our experiments confirm that this method is able to substantially better approximate the Shannon entropy compared to the prior state-of-the-art.

43. Fast Near Neighbor Search in High-Dimensional Binary Data

    Anshumali Shrivastava and Ping Li

    In The European Conference on Machine Learning (ECML 2012), 2012

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    Abstract. Numerous applications in search, databases, machine learning, and computer vision, can benefit from efficient algorithms for near neighbor search. This paper proposes a simple framework for fast near neighbor search in high-dimensional binary data, which are common in practice (e.g., text). We develop a very simple and effective strategy for sub-linear time near neighbor search, by creating hash tables directly using the bits generated by b-bit minwise hashing. The advantages of our method are demonstrated through thorough comparisons with two strong baselines: spectral hashing and sign (1-bit) random projections.

44. Testing for Membership to the IFRA and the NBU Classes of Distributions

    Radhendushka Srivastava, Ping Li, and Debasis Sengupta

    Journal of Machine Learning Research - Proceedings Track for the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), 2012

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    This paper provides test procedures to determine whether the probability distribution underlying a set of non-negative valued samples belongs to the Increasing Failure Rate Average (IFRA) class or the New Better than Used (NBU) class. Membership of a distribution to one of these classes is known to have implications which are important in reliability, queuing theory, game theory and other disciplines. Our proposed test is based on the Kolmogorov-Smirnov distance between an empirical cumulative hazard function and its best approximation from the class of distributions constituting the null hypothesis. It turns out that the least favorable distribution, which produces the largest probability of Type I error of each of the tests, is the exponential distribution. This fact is used to produce an appropriate cut-off or p-value. Monte Carlo simulations are conducted to check small sample size (i.e., significance) and power of the test. Usefulness of the test is illustrated through the analysis of a set of monthly family expenditure data collected by the National Sample Survey Organization of the Government of India.

45. Fast Multi-task Learning for Query Spelling Correction

    Xu Sun, Anshumali Shrivastava, and Ping Li

    In The 21^st ACM International Conference on Information and Knowledge Management (CIKM 2012) , 2012

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    In this paper, we explore the use of a novel online multi-task learning framework for the task of search query spelling correction. In our procedure, correction candidates are initially generated by a ranker-based system and then re-ranked by our multi-task learning algorithm. With the proposed multi-task learning method, we are able to effectively transfer information from different and highly biased training datasets, for improving spelling correction on all datasets. Our experiments are conducted on three query spelling correction datasets including the well-known TREC benchmark dataset. The experimental results demonstrate that our proposed method considerably outperforms the existing baseline systems in terms of accuracy. Importantly, the proposed method is about one order of magnitude faster than baseline systems in terms of training speed. Compared to the commonly used online learning methods which typically require more than (e.g.,) 60 training passes, our proposed method is able to closely reach the empirical optimum in about 5 passes.

46. Query spelling correction using multi-task learning

    Xu Sun, Anshumali Shrivastava, and Ping Li

    In Proceedings of the 21st World Wide Web Conference (WWW 2012)(Companion Volume), 2012

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    This paper explores the use of online multi-task learning for search query spelling correction, by effectively transferring information from different and biased training datasets for improving spelling correction across datasets. Experiments were conducted on three query spelling correction datasets, including the well-known TREC benchmark data. Our experimental results demonstrate that the proposed method considerably outperforms existing baseline systems in terms of accuracy. Importantly, the proposed method is about one-order of magnitude faster than baseline systems in terms of training speed. In contrast to existing methods which typically require more than (e.g.,) 50 training passes, our algorithm can very closely approach the empirical optimum in around five passes.