{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:01:03Z","timestamp":1764403263272},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p>\n            When analyzing confidential data through a privacy filter, a data scientist often needs to decide which queries will best support their intended analysis. For example, an analyst may wish to study noisy two-way marginals in a dataset produced by a mechanism M\n            <jats:sub>1<\/jats:sub>\n            . But, if the data are relatively sparse, the analyst may choose to examine noisy one-way marginals, produced by a mechanism M\n            <jats:sub>2<\/jats:sub>\n            , instead. Since the choice of whether to use M\n            <jats:sub>1<\/jats:sub>\n            or M\n            <jats:sub>2<\/jats:sub>\n            is data-dependent, a typical differentially private workflow is to first split the privacy loss budget\n            <jats:italic>\u03c1<\/jats:italic>\n            into two parts:\n            <jats:italic>\u03c1<\/jats:italic>\n            <jats:sub>1<\/jats:sub>\n            and\n            <jats:italic>\u03c1<\/jats:italic>\n            <jats:sub>2<\/jats:sub>\n            , then use the first part\n            <jats:italic>\u03c1<\/jats:italic>\n            <jats:sub>1<\/jats:sub>\n            to determine which mechanism to use, and the remainder\n            <jats:italic>\u03c1<\/jats:italic>\n            <jats:sub>2<\/jats:sub>\n            to obtain noisy answers from the chosen mechanism. In a sense, the first step seems wasteful because it takes away part of the privacy loss budget that could have been used to make the query answers more accurate.\n          <\/jats:p>\n          <jats:p>\n            In this paper, we consider the question of whether the choice between M\n            <jats:sub>1<\/jats:sub>\n            and M\n            <jats:sub>2<\/jats:sub>\n            can be performed without wasting any privacy loss budget. For linear queries, we propose a method for decomposing M\n            <jats:sub>1<\/jats:sub>\n            and M\n            <jats:sub>2<\/jats:sub>\n            into three parts: (1) a mechanism M\n            <jats:sub>*<\/jats:sub>\n            that captures their shared information, (2) a mechanism M\u20321 that captures information that is specific to M\n            <jats:sub>1<\/jats:sub>\n            , (3) a mechanism M\u20322 that captures information that is specific to M\n            <jats:sub>2<\/jats:sub>\n            . Running M\n            <jats:sub>*<\/jats:sub>\n            and M\u2032\n            <jats:sub>1<\/jats:sub>\n            together is completely equivalent to running M\n            <jats:sub>1<\/jats:sub>\n            (both in terms of query answer accuracy and total privacy cost\n            <jats:italic>\u03c1<\/jats:italic>\n            ). Similarly, running M\n            <jats:sub>*<\/jats:sub>\n            and M\u2032\n            <jats:sub>2<\/jats:sub>\n            together is completely equivalent to running M\n            <jats:sub>2<\/jats:sub>\n            .\n          <\/jats:p>\n          <jats:p>\n            Since M\n            <jats:sub>*<\/jats:sub>\n            will be used no matter what, the analyst can use its output to decide whether to subsequently run\n            <jats:italic>M<\/jats:italic>\n            \u2032\n            <jats:sub>1<\/jats:sub>\n            (thus recreating the analysis supported by M\n            <jats:sub>1<\/jats:sub>\n            )or M\u2032\n            <jats:sub>2<\/jats:sub>\n            (recreating the analysis supported by M\n            <jats:sub>2<\/jats:sub>\n            ), without wasting privacy loss budget.\n          <\/jats:p>","DOI":"10.14778\/3594512.3594519","type":"journal-article","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:28:36Z","timestamp":1687480116000},"page":"1883-1896","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Answering Private Linear Queries Adaptively Using the Common Mechanism"],"prefix":"10.14778","volume":"16","author":[{"given":"Yingtai","family":"Xiao","sequence":"first","affiliation":[{"name":"Penn State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanhong","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Penn State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Kifer","sequence":"additional","affiliation":[{"name":"Penn State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"John M. Abowd Robert Ashmead Ryan Cumings-Menon Simson Garfinkel Micah Heineck Christine Heiss Robert Johns Daniel Kifer Philip Leclerc Ashwin Machanavajjhala Brett Moran William Sexton Matthew Spence and Pavel Zhuravlev. forthcoming. Preprint https:\/\/www.census.gov\/library\/working-papers\/2022\/adrm\/CED-WP-2022-002.html. The 2020 Census Disclosure Avoidance System TopDown Algorithm. Harvard Data Science Review (forthcoming. Preprint https:\/\/www.census.gov\/library\/working-papers\/2022\/adrm\/CED-WP-2022-002.html).  John M. Abowd Robert Ashmead Ryan Cumings-Menon Simson Garfinkel Micah Heineck Christine Heiss Robert Johns Daniel Kifer Philip Leclerc Ashwin Machanavajjhala Brett Moran William Sexton Matthew Spence and Pavel Zhuravlev. forthcoming. Preprint https:\/\/www.census.gov\/library\/working-papers\/2022\/adrm\/CED-WP-2022-002.html. The 2020 Census Disclosure Avoidance System TopDown Algorithm. Harvard Data Science Review (forthcoming. Preprint https:\/\/www.census.gov\/library\/working-papers\/2022\/adrm\/CED-WP-2022-002.html).","DOI":"10.1162\/99608f92.529e3cb9"},{"key":"e_1_2_1_2_1","volume-title":"2012 IEEE 12th International Conference on Data Mining. 1--10","author":"Acs Gergely","year":"2012","unstructured":"Gergely Acs , Claude Castelluccia , and Rui Chen . 2012 . Differentially private histogram publishing through lossy compression . In 2012 IEEE 12th International Conference on Data Mining. 1--10 . Gergely Acs, Claude Castelluccia, and Rui Chen. 2012. Differentially private histogram publishing through lossy compression. In 2012 IEEE 12th International Conference on Data Mining. 1--10."},{"key":"e_1_2_1_3_1","first-page":"5","article-title":"Hit and run as a unifying device","volume":"148","author":"Andersen Hans C.","year":"2007","unstructured":"Hans C. Andersen and Persi Diaconis . 2007 . Hit and run as a unifying device . Journal de la soci\u00e9t\u00e9 fran\u00e7aise de statistique 148 , 4 (2007), 5 -- 28 . http:\/\/eudml.org\/doc\/93471 Hans C. Andersen and Persi Diaconis. 2007. Hit and run as a unifying device. Journal de la soci\u00e9t\u00e9 fran\u00e7aise de statistique 148, 4 (2007), 5--28. http:\/\/eudml.org\/doc\/93471","journal-title":"Journal de la soci\u00e9t\u00e9 fran\u00e7aise de statistique"},{"key":"e_1_2_1_4_1","volume-title":"International Conference on Machine Learning. PMLR, 457--467","author":"Aydore Sergul","year":"2021","unstructured":"Sergul Aydore , William Brown , Michael Kearns , Krishnaram Kenthapadi , Luca Melis , Aaron Roth , and Ankit A Siva . 2021 . Differentially private query release through adaptive projection . In International Conference on Machine Learning. PMLR, 457--467 . Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, and Ankit A Siva. 2021. Differentially private query release through adaptive projection. In International Conference on Machine Learning. PMLR, 457--467."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4086\/toc.2016.v012a001","article-title":"Private Learning and Sanitization: Pure vs. Approximate Differential Privacy","volume":"12","author":"Beimel Amos","year":"2016","unstructured":"Amos Beimel , Kobbi Nissim , and Uri Stemmer . 2016 . Private Learning and Sanitization: Pure vs. Approximate Differential Privacy . Theory of Computing 12 , 1 (2016), 1 -- 61 . Amos Beimel, Kobbi Nissim, and Uri Stemmer. 2016. Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. Theory of Computing 12, 1 (2016), 1--61.","journal-title":"Theory of Computing"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","author":"Bhaskar Raghav","year":"2010","unstructured":"Raghav Bhaskar , Srivatsan Laxman , Adam Smith , and Abhradeep Thakurta . 2010 . Discovering Frequent Patterns in Sensitive Data . In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta. 2010. Discovering Frequent Patterns in Sensitive Data. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_2_1_7_1","volume-title":"Private hypothesis selection. Advances in Neural Information Processing Systems 32","author":"Bun Mark","year":"2019","unstructured":"Mark Bun , Gautam Kamath , Thomas Steinke , and Steven Z Wu. 2019. Private hypothesis selection. Advances in Neural Information Processing Systems 32 ( 2019 ). Mark Bun, Gautam Kamath, Thomas Steinke, and Steven Z Wu. 2019. Private hypothesis selection. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings, Part I, of the 14th International Conference on Theory of Cryptography -","volume":"9985","author":"Bun Mark","year":"2016","unstructured":"Mark Bun and Thomas Steinke . 2016 . Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds . In Proceedings, Part I, of the 14th International Conference on Theory of Cryptography - Volume 9985 . Mark Bun and Thomas Steinke. 2016. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. In Proceedings, Part I, of the 14th International Conference on Theory of Cryptography - Volume 9985."},{"key":"e_1_2_1_9_1","unstructured":"U.S. Census Bureau. [n.d.]. Decennial Census: 2010 Summary Files. https:\/\/www.census.gov\/mp\/www\/cat\/decennial_census_2010\/.  U.S. Census Bureau. [n.d.]. Decennial Census: 2010 Summary Files. https:\/\/www.census.gov\/mp\/www\/cat\/decennial_census_2010\/."},{"key":"e_1_2_1_10_1","unstructured":"U. S. Census Bureau. [n.d.]. On The Map: Longitudinal Employer-Household Dynamics. https:\/\/lehd.ces.census.gov\/applications\/help\/onthemap.html#!confidentiality_protection.  U. S. Census Bureau. [n.d.]. On The Map: Longitudinal Employer-Household Dynamics. https:\/\/lehd.ces.census.gov\/applications\/help\/onthemap.html#!confidentiality_protection."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.14778\/3476249.3476272","article-title":"Data synthesis via differentially private markov random fields","volume":"14","author":"Cai Kuntai","year":"2021","unstructured":"Kuntai Cai , Xiaoyu Lei , Jianxin Wei , and Xiaokui Xiao . 2021 . Data synthesis via differentially private markov random fields . Proceedings of the VLDB Endowment 14 , 11 (2021), 2190 -- 2202 . Kuntai Cai, Xiaoyu Lei, Jianxin Wei, and Xiaokui Xiao. 2021. Data synthesis via differentially private markov random fields. Proceedings of the VLDB Endowment 14, 11 (2021), 2190--2202.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_12_1","unstructured":"Clement L Canonne Gautam Kamath and Thomas Steinke. 2020. The Discrete Gaussian for Differential Privacy. In NeurIPS.  Clement L Canonne Gautam Kamath and Thomas Steinke. 2020. The Discrete Gaussian for Differential Privacy. In NeurIPS."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18128\/D020.V7.3"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems -","volume":"1","author":"Chaudhuri Kamalika","year":"2014","unstructured":"Kamalika Chaudhuri , Daniel Hsu , and Shuang Song . 2014 . The Large Margin Mechanism for Differentially Private Maximization . In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1 . Kamalika Chaudhuri, Daniel Hsu, and Shuang Song. 2014. The Large Margin Mechanism for Differentially Private Maximization. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1."},{"key":"e_1_2_1_15_1","volume-title":"Journal of Machine Learning Research","author":"Chaudhuri Kamalika","year":"2011","unstructured":"Kamalika Chaudhuri , Claire Monteleoni , and Anand D Sarwate . 2011 . Differentially private empirical risk minimization . Journal of Machine Learning Research 12, Mar (2011), 1069--1109. Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011. Differentially private empirical risk minimization. Journal of Machine Learning Research 12, Mar (2011), 1069--1109."},{"key":"e_1_2_1_16_1","volume-title":"Differentially Private Regression Diagnostics. In IEEE 16th International Conference on Data Mining (ICDM).","author":"Chen Yan","unstructured":"Yan Chen , Ashwin Machanavajjhala , Jerome P. Reiter , and Andr\u00e9s F. Barrientos . 2016 . Differentially Private Regression Diagnostics. In IEEE 16th International Conference on Data Mining (ICDM). Yan Chen, Ashwin Machanavajjhala, Jerome P. Reiter, and Andr\u00e9s F. Barrientos. 2016. Differentially Private Regression Diagnostics. In IEEE 16th International Conference on Data Mining (ICDM)."},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Ding Bolin","year":"2017","unstructured":"Bolin Ding , Janardhan Kulkarni , and Sergey Yekhanin . 2017 . Collecting Telemetry Data Privately . In Proceedings of the 31st International Conference on Neural Information Processing Systems ( Long Beach, California, USA) (NIPS'17). Curran Associates Inc., USA, 3574--3583. http:\/\/dl.acm.org\/citation.cfm?id=3294996.3295115 Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. 2017. Collecting Telemetry Data Privately. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS'17). Curran Associates Inc., USA, 3574--3583. http:\/\/dl.acm.org\/citation.cfm?id=3294996.3295115"},{"key":"e_1_2_1_18_1","volume-title":"Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms. The VLDB Journal","author":"Ding Zeyu","year":"2022","unstructured":"Zeyu Ding , Yuxin Wang , Yingtai Xiao , Guanhong Wang , Danfeng Zhang , and Daniel Kifer . 2022. Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms. The VLDB Journal ( 2022 ). Zeyu Ding, Yuxin Wang, Yingtai Xiao, Guanhong Wang, Danfeng Zhang, and Daniel Kifer. 2022. Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms. The VLDB Journal (2022)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/3368289.3368295"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12454"},{"key":"e_1_2_1_21_1","volume-title":"Our Data","author":"Dwork Cynthia","unstructured":"Cynthia Dwork , Krishnaram Kenthapadi , Frank McSherry , Ilya Mironov , and Moni Naor . 2006. Our Data , Ourselves : Privacy via Distributed Noise Generation. In EUROCRYPT. 486--503. Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. 2006. Our Data, Ourselves: Privacy via Distributed Noise Generation. In EUROCRYPT. 486--503."},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis.. In TCC.  Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis.. In TCC.","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"e_1_2_1_24_1","volume-title":"Differential Privacy: Now It's Getting Personal. In POPL.","author":"Ebadi Hamid","year":"2015","unstructured":"Hamid Ebadi , David Sands , and Gerardo Schneider . 2015 . Differential Privacy: Now It's Getting Personal. In POPL. Hamid Ebadi, David Sands, and Gerardo Schneider. 2015. Differential Privacy: Now It's Getting Personal. In POPL."},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing. 425--438","author":"Edmonds Alexander","year":"2020","unstructured":"Alexander Edmonds , Aleksandar Nikolov , and Jonathan Ullman . 2020 . The power of factorization mechanisms in local and central differential privacy . In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing. 425--438 . Alexander Edmonds, Aleksandar Nikolov, and Jonathan Ullman. 2020. The power of factorization mechanisms in local and central differential privacy. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing. 425--438."},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security","author":"Erlingsson \u00dalfar","year":"2014","unstructured":"\u00dalfar Erlingsson , Vasyl Pihur , and Aleksandra Korolova . 2014 . RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response . In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security ( Scottsdale, Arizona, USA) (CCS '14). ACM, New York, NY, USA, 1054--1067. \u00dalfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (Scottsdale, Arizona, USA) (CCS '14). ACM, New York, NY, USA, 1054--1067."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the 2019 International Conference on Management of Data","author":"Ge Chang","year":"2019","unstructured":"Chang Ge , Xi He , Ihab F. Ilyas , and Ashwin Machanavajjhala . 2019 . APEx: Accuracy-Aware Differentially Private Data Exploration . In Proceedings of the 2019 International Conference on Management of Data ( Amsterdam, Netherlands) (SIGMOD '19). Association for Computing Machinery, New York, NY, USA, 177--194. 10.1145\/3299869.3300092 Chang Ge, Xi He, Ihab F. Ilyas, and Ashwin Machanavajjhala. 2019. APEx: Accuracy-Aware Differentially Private Data Exploration. In Proceedings of the 2019 International Conference on Management of Data (Amsterdam, Netherlands) (SIGMOD '19). Association for Computing Machinery, New York, NY, USA, 177--194. 10.1145\/3299869.3300092"},{"key":"e_1_2_1_28_1","unstructured":"Google. [n.d.]. https:\/\/github.com\/tensorflow\/privacy.  Google. [n.d.]. https:\/\/github.com\/tensorflow\/privacy."},{"key":"e_1_2_1_29_1","volume-title":"Differentially Private Algorithms for 2020 Census Detailed DHC Race \\& Ethnicity. CoRR abs\/2107.10659","author":"Haney Samuel","year":"2021","unstructured":"Samuel Haney , William Sexton , Ashwin Machanavajjhala , Michael Hay , and Gerome Miklau . 2021. Differentially Private Algorithms for 2020 Census Detailed DHC Race \\& Ethnicity. CoRR abs\/2107.10659 ( 2021 ). arXiv:2107.10659 https:\/\/arxiv.org\/abs\/2107.10659 Samuel Haney, William Sexton, Ashwin Machanavajjhala, Michael Hay, and Gerome Miklau. 2021. Differentially Private Algorithms for 2020 Census Detailed DHC Race \\& Ethnicity. CoRR abs\/2107.10659 (2021). arXiv:2107.10659 https:\/\/arxiv.org\/abs\/2107.10659"},{"key":"e_1_2_1_30_1","volume-title":"A simple and practical algorithm for differentially private data release. Advances in neural information processing systems 25","author":"Hardt Moritz","year":"2012","unstructured":"Moritz Hardt , Katrina Ligett , and Frank McSherry . 2012. A simple and practical algorithm for differentially private data release. Advances in neural information processing systems 25 ( 2012 ). Moritz Hardt, Katrina Ligett, and Frank McSherry. 2012. A simple and practical algorithm for differentially private data release. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the 2016 International Conference on Management of Data. 139--154","author":"Hay Michael","year":"2016","unstructured":"Michael Hay , Ashwin Machanavajjhala , Gerome Miklau , Yan Chen , and Dan Zhang . 2016 . Principled evaluation of differentially private algorithms using dpbench . In Proceedings of the 2016 International Conference on Management of Data. 139--154 . Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, and Dan Zhang. 2016. Principled evaluation of differentially private algorithms using dpbench. In Proceedings of the 2016 International Conference on Management of Data. 139--154."},{"key":"e_1_2_1_32_1","first-page":"1","volume-title":"Proceedings of the VLDB Endowment 3","author":"Hay Michael","year":"2010","unstructured":"Michael Hay , Vibhor Rastogi , Gerome Miklau , and Dan Suciu . 2010 . Boosting the Accuracy of Differentially Private Histograms Through Consistency . Proceedings of the VLDB Endowment 3 , 1 (2010). Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu. 2010. Boosting the Accuracy of Differentially Private Histograms Through Consistency. Proceedings of the VLDB Endowment 3, 1 (2010)."},{"key":"e_1_2_1_33_1","volume-title":"Matrix analysis","author":"Horn Roger A","unstructured":"Roger A Horn and Charles R Johnson . 2012. Matrix analysis . Cambridge university press . Roger A Horn and Charles R Johnson. 2012. Matrix analysis. Cambridge university press."},{"key":"e_1_2_1_34_1","unstructured":"Batta (https:\/\/math.stackexchange.com\/users\/488522\/batta). [n.d.]. Linear Algebra Vector Space: how to find intersection of two subspaces? Mathematics Stack Exchange. arXiv:https:\/\/math.stackexchange.com\/q\/2477195 https:\/\/math.stackexchange.com\/q\/2477195 URL:https:\/\/math.stackexchange.com\/q\/2477195 (version: 2017-10-18).  Batta (https:\/\/math.stackexchange.com\/users\/488522\/batta). [n.d.]. Linear Algebra Vector Space: how to find intersection of two subspaces? Mathematics Stack Exchange. arXiv:https:\/\/math.stackexchange.com\/q\/2477195 https:\/\/math.stackexchange.com\/q\/2477195 URL:https:\/\/math.stackexchange.com\/q\/2477195 (version: 2017-10-18)."},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1145\/3187009.3177733","article-title":"Towards practical differential privacy for SQL queries","volume":"11","author":"Johnson Noah","year":"2018","unstructured":"Noah Johnson , Joseph P Near , and Dawn Song . 2018 . Towards practical differential privacy for SQL queries . Proceedings of the VLDB Endowment 11 , 5 (2018), 526 -- 539 . Noah Johnson, Joseph P Near, and Dawn Song. 2018. Towards practical differential privacy for SQL queries. Proceedings of the VLDB Endowment 11, 5 (2018), 526--539.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_36_1","volume-title":"Chorus: Differential Privacy via Query Rewriting. CoRR abs\/1809.07750","author":"Johnson Noah M.","year":"2018","unstructured":"Noah M. Johnson , Joseph P. Near , Joseph M. Hellerstein , and Dawn Song . 2018 . Chorus: Differential Privacy via Query Rewriting. CoRR abs\/1809.07750 (2018). arXiv:1809.07750 http:\/\/arxiv.org\/abs\/1809.07750 Noah M. Johnson, Joseph P. Near, Joseph M. Hellerstein, and Dawn Song. 2018. Chorus: Differential Privacy via Query Rewriting. CoRR abs\/1809.07750 (2018). arXiv:1809.07750 http:\/\/arxiv.org\/abs\/1809.07750"},{"key":"e_1_2_1_37_1","unstructured":"Daniel Kifer John M. Abowd Robert Ashmead Ryan Cumings-Menon Philip Leclerc Ashwin Machanavajjhala William Sexton and Pavel Zhuravlev. 2022. Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census. 10.48550\/ARXIV.2209.03310  Daniel Kifer John M. Abowd Robert Ashmead Ryan Cumings-Menon Philip Leclerc Ashwin Machanavajjhala William Sexton and Pavel Zhuravlev. 2022. Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census. 10.48550\/ARXIV.2209.03310"},{"key":"e_1_2_1_38_1","volume-title":"Gradual release of sensitive data under differential privacy. arXiv preprint arXiv:1504.00429","author":"Koufogiannis Fragkiskos","year":"2015","unstructured":"Fragkiskos Koufogiannis , Shuo Han , and George J Pappas . 2015. Gradual release of sensitive data under differential privacy. arXiv preprint arXiv:1504.00429 ( 2015 ). Fragkiskos Koufogiannis, Shuo Han, and George J Pappas. 2015. Gradual release of sensitive data under differential privacy. arXiv preprint arXiv:1504.00429 (2015)."},{"key":"e_1_2_1_39_1","first-page":"5","volume-title":"Proceedings of the VLDB Endowment 7","author":"Li Chao","year":"2014","unstructured":"Chao Li , Michael Hay , Gerome Miklau , and Yue Wang . 2014 . A Data-and Workload-Aware Algorithm for Range Queries Under Differential Privacy . Proceedings of the VLDB Endowment 7 , 5 (2014). Chao Li, Michael Hay, Gerome Miklau, and Yue Wang. 2014. A Data-and Workload-Aware Algorithm for Range Queries Under Differential Privacy. Proceedings of the VLDB Endowment 7, 5 (2014)."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-015-0398-x"},{"key":"e_1_2_1_41_1","first-page":"1598","article-title":"Enabling multilevel trust in privacy preserving data mining","volume":"24","author":"Li Yaping","year":"2011","unstructured":"Yaping Li , Minghua Chen , Qiwei Li , and Wei Zhang . 2011 . Enabling multilevel trust in privacy preserving data mining . IEEE Transactions on Knowledge and Data Engineering 24 , 9 (2011), 1598 -- 1612 . Yaping Li, Minghua Chen, Qiwei Li, and Wei Zhang. 2011. Enabling multilevel trust in privacy preserving data mining. IEEE Transactions on Knowledge and Data Engineering 24, 9 (2011), 1598--1612.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_1_42_1","volume-title":"Wu","author":"Ligett Katrina","year":"2017","unstructured":"Katrina Ligett , Seth Neel , Aaron Roth , Bo Waggoner , and Steven Z . Wu . 2017 . Accuracy First : Selecting a Differential Privacy Level for Accuracy Constrained ERM. In NIPS. Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, and Steven Z. Wu. 2017. Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. In NIPS."},{"key":"e_1_2_1_43_1","volume-title":"Private Selection from Private Candidates. arXiv preprint arXiv:1811.07971","author":"Liu Jingcheng","year":"2018","unstructured":"Jingcheng Liu and Kunal Talwar . 2018. Private Selection from Private Candidates. arXiv preprint arXiv:1811.07971 ( 2018 ). Jingcheng Liu and Kunal Talwar. 2018. Private Selection from Private Candidates. arXiv preprint arXiv:1811.07971 (2018)."},{"key":"e_1_2_1_44_1","volume-title":"International Conference on Machine Learning. PMLR, 6968--6977","author":"Liu Terrance","year":"2021","unstructured":"Terrance Liu , Giuseppe Vietri , Thomas Steinke , Jonathan Ullman , and Steven Wu . 2021 . Leveraging public data for practical private query release . In International Conference on Machine Learning. PMLR, 6968--6977 . Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Steven Wu. 2021. Leveraging public data for practical private query release. In International Conference on Machine Learning. PMLR, 6968--6977."},{"key":"e_1_2_1_45_1","volume-title":"Iterative methods for private synthetic data: Unifying framework and new methods. Advances in Neural Information Processing Systems 34","author":"Liu Terrance","year":"2021","unstructured":"Terrance Liu , Giuseppe Vietri , and Steven Z Wu. 2021. Iterative methods for private synthetic data: Unifying framework and new methods. Advances in Neural Information Processing Systems 34 ( 2021 ). Terrance Liu, Giuseppe Vietri, and Steven Z Wu. 2021. Iterative methods for private synthetic data: Unifying framework and new methods. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_2_1_46_1","unstructured":"Terrance Liu and Steven Wu. 2022. Towards Differentially Private Query Release for Hierarchical Data. In ICLR 2022 Workshop on PAIR^2Struct: Privacy Accountability Interpretability Robustness Reasoning on Structured Data. https:\/\/openreview.net\/forum?id=BOulQJ9hLlq  Terrance Liu and Steven Wu. 2022. Towards Differentially Private Query Release for Hierarchical Data. In ICLR 2022 Workshop on PAIR^2Struct: Privacy Accountability Interpretability Robustness Reasoning on Structured Data. https:\/\/openreview.net\/forum?id=BOulQJ9hLlq"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1006\/jsco.1996.0092"},{"key":"e_1_2_1_48_1","first-page":"637","article-title":"Understanding the sparse vector technique for differential privacy","volume":"10","author":"Lyu Min","year":"2017","unstructured":"Min Lyu , Dong Su , and Ninghui Li . 2017 . Understanding the sparse vector technique for differential privacy . PVLDB 10 , 6 (2017), 637 -- 648 . Min Lyu, Dong Su, and Ninghui Li. 2017. Understanding the sparse vector technique for differential privacy. PVLDB 10, 6 (2017), 637--648.","journal-title":"PVLDB"},{"key":"e_1_2_1_49_1","volume-title":"Proceedings of the IEEE International Conference on Data Engineering (ICDE). 277--286","author":"Machanavajjhala Ashwin","year":"2008","unstructured":"Ashwin Machanavajjhala , Daniel Kifer , John Abowd , Johannes Gehrke , and Lars Vilhuber . 2008 . Privacy: From Theory to Practice On the Map . In Proceedings of the IEEE International Conference on Data Engineering (ICDE). 277--286 . Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, and Lars Vilhuber. 2008. Privacy: From Theory to Practice On the Map. In Proceedings of the IEEE International Conference on Data Engineering (ICDE). 277--286."},{"key":"e_1_2_1_50_1","unstructured":"Miti Mazmudar Thomas Humphries Matthew Rafuse and Xi He. 2020. Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration. In TPDP.  Miti Mazmudar Thomas Humphries Matthew Rafuse and Xi He. 2020. Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration. In TPDP."},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the VLDB Endowment 11","author":"McKenna Ryan","year":"2018","unstructured":"Ryan McKenna , Gerome Miklau , Michael Hay , and Ashwin Machanavajjhala . 2018 . Optimizing error of high-dimensional statistical queries under differential privacy . Proceedings of the VLDB Endowment 11 , 10 (2018). Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala. 2018. Optimizing error of high-dimensional statistical queries under differential privacy. Proceedings of the VLDB Endowment 11, 10 (2018)."},{"key":"e_1_2_1_52_1","volume-title":"Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. arXiv preprint arXiv:2108.04978","author":"McKenna Ryan","year":"2021","unstructured":"Ryan McKenna , Gerome Miklau , and Daniel Sheldon . 2021. Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. arXiv preprint arXiv:2108.04978 ( 2021 ). Ryan McKenna, Gerome Miklau, and Daniel Sheldon. 2021. Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. arXiv preprint arXiv:2108.04978 (2021)."},{"key":"e_1_2_1_53_1","volume-title":"AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. arXiv preprint arXiv:2201.12677","author":"McKenna Ryan","year":"2022","unstructured":"Ryan McKenna , Brett Mullins , Daniel Sheldon , and Gerome Miklau . 2022 . AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. arXiv preprint arXiv:2201.12677 (2022). Ryan McKenna, Brett Mullins, Daniel Sheldon, and Gerome Miklau. 2022. AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. arXiv preprint arXiv:2201.12677 (2022)."},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS '07)","author":"McSherry Frank","year":"2007","unstructured":"Frank McSherry and Kunal Talwar . 2007 . Mechanism Design via Differential Privacy . In Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS '07) . IEEE Computer Society, Washington, DC, USA, 94--103. Frank McSherry and Kunal Talwar. 2007. Mechanism Design via Differential Privacy. In Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS '07). IEEE Computer Society, Washington, DC, USA, 94--103."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.7910\/DVN\/EIAACS\/PMQG9X"},{"key":"e_1_2_1_56_1","volume-title":"R\u00e9nyi Differential Privacy. In 30th IEEE Computer Security Foundations Symposium, CSF 2017","author":"Mironov Ilya","year":"2017","unstructured":"Ilya Mironov . 2017 . R\u00e9nyi Differential Privacy. In 30th IEEE Computer Security Foundations Symposium, CSF 2017 , Santa Barbara, CA, USA, August 21--25 , 2017. 263--275. Ilya Mironov. 2017. R\u00e9nyi Differential Privacy. In 30th IEEE Computer Security Foundations Symposium, CSF 2017, Santa Barbara, CA, USA, August 21--25, 2017. 263--275."},{"key":"e_1_2_1_57_1","unstructured":"Opacus [n.d.]. Opacus PyTorch library. Available from opacus.ai.  Opacus [n.d.]. Opacus PyTorch library. Available from opacus.ai."},{"key":"e_1_2_1_58_1","volume-title":"Smith","author":"Raskhodnikova Sofya","year":"2016","unstructured":"Sofya Raskhodnikova and Adam D . Smith . 2016 . Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism. In FOCS. IEEE Computer Society , 495--504. Sofya Raskhodnikova and Adam D. Smith. 2016. Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism. In FOCS. IEEE Computer Society, 495--504."},{"key":"e_1_2_1_59_1","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1287\/opre.32.6.1296","article-title":"Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions","volume":"32","author":"Smith Robert L.","year":"1984","unstructured":"Robert L. Smith . 1984 . Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions . Operations Research 32 , 6 (1984), 1296 -- 1308 . http:\/\/www.jstor.org\/stable\/170949 Robert L. Smith. 1984. Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions. Operations Research 32, 6 (1984), 1296--1308. http:\/\/www.jstor.org\/stable\/170949","journal-title":"Operations Research"},{"key":"e_1_2_1_60_1","volume-title":"2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 552--563","author":"Steinke Thomas","year":"2017","unstructured":"Thomas Steinke and Jonathan Ullman . 2017 . Tight lower bounds for differentially private selection . In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 552--563 . Thomas Steinke and Jonathan Ullman. 2017. Tight lower bounds for differentially private selection. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 552--563."},{"key":"e_1_2_1_61_1","volume-title":"Maximal lower bounds in the L\\\" owner order. arXiv preprint arXiv:1612.05664","author":"Stott Nikolas","year":"2016","unstructured":"Nikolas Stott . 2016. Maximal lower bounds in the L\\\" owner order. arXiv preprint arXiv:1612.05664 ( 2016 ). Nikolas Stott. 2016. Maximal lower bounds in the L\\\" owner order. arXiv preprint arXiv:1612.05664 (2016)."},{"key":"e_1_2_1_64_1","article-title":"Learning with Privacy at Scale","volume":"1","author":"Privacy Team Apple Differential","year":"2017","unstructured":"Apple Differential Privacy Team . 2017 . Learning with Privacy at Scale . Apple Machine Learning Journal 1 , 8 (2017). Apple Differential Privacy Team. 2017. Learning with Privacy at Scale. Apple Machine Learning Journal 1, 8 (2017).","journal-title":"Apple Machine Learning Journal"},{"key":"e_1_2_1_65_1","volume-title":"Proceedings of the 26th Annual Conference on Learning Theory.","author":"Thakurta Abhradeep Guha","year":"2013","unstructured":"Abhradeep Guha Thakurta and Adam Smith . 2013 . Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso . In Proceedings of the 26th Annual Conference on Learning Theory. Abhradeep Guha Thakurta and Adam Smith. 2013. Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. In Proceedings of the 26th Annual Conference on Learning Theory."},{"key":"e_1_2_1_66_1","volume-title":"Proceedings on Privacy Enhancing Technologies Symposium.","author":"Wilson Royce","year":"2020","unstructured":"Royce Wilson , Celia Yuxin Zhang , William Lam , Damien Desfontaines , Daniel Simmons-Marengo , and Bryant Gipson . 2020 . Differentially Private SQL with Bounded User Contribution . In Proceedings on Privacy Enhancing Technologies Symposium. Royce Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson. 2020. Differentially Private SQL with Bounded User Contribution. In Proceedings on Privacy Enhancing Technologies Symposium."},{"key":"e_1_2_1_67_1","volume-title":"Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. 229--240","author":"Xiao Xiaokui","year":"2011","unstructured":"Xiaokui Xiao , Gabriel Bender , Michael Hay , and Johannes Gehrke . 2011 . iReduct: Differential privacy with reduced relative errors . In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. 229--240 . Xiaokui Xiao, Gabriel Bender, Michael Hay, and Johannes Gehrke. 2011. iReduct: Differential privacy with reduced relative errors. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. 229--240."},{"key":"e_1_2_1_68_1","first-page":"1","article-title":"Optimal Random Perturbation at Multiple Privacy Levels","volume":"2","author":"Xiao Xiaokui","year":"2009","unstructured":"Xiaokui Xiao , Yufei Tao , and Minghua Chen . 2009 . Optimal Random Perturbation at Multiple Privacy Levels . Proc. VLDB Endow. 2 , 1 (aug 2009), 814--825. Xiaokui Xiao, Yufei Tao, and Minghua Chen. 2009. Optimal Random Perturbation at Multiple Privacy Levels. Proc. VLDB Endow. 2, 1 (aug 2009), 814--825.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_69_1","doi-asserted-by":"crossref","unstructured":"Yingtai Xiao Zeyu Ding Yuxin Wang Danfeng Zhang and Daniel Kifer. 2021. Optimizing fitness-for-use of differentially private linear queries. In VLDB.  Yingtai Xiao Zeyu Ding Yuxin Wang Danfeng Zhang and Daniel Kifer. 2021. Optimizing fitness-for-use of differentially private linear queries. In VLDB.","DOI":"10.14778\/3467861.3467864"},{"key":"e_1_2_1_70_1","unstructured":"Yingtai Xiao Guanhong Wang Danfeng Zhang and Daniel Kifer. 2022. Answering Private Linear Queries Adaptively using the Common Mechanism. 10.48550\/ARXIV.2212.00135  Yingtai Xiao Guanhong Wang Danfeng Zhang and Daniel Kifer. 2022. Answering Private Linear Queries Adaptively using the Common Mechanism. 10.48550\/ARXIV.2212.00135"},{"key":"e_1_2_1_71_1","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s00778-013-0309-y","article-title":"Differentially private histogram publication","volume":"22","author":"Xu Jia","year":"2013","unstructured":"Jia Xu , Zhenjie Zhang , Xiaokui Xiao , Yin Yang , Ge Yu , and Marianne Winslett . 2013 . Differentially private histogram publication . The VLDB journal 22 , 6 (2013), 797 -- 822 . Jia Xu, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, Ge Yu, and Marianne Winslett. 2013. Differentially private histogram publication. The VLDB journal 22, 6 (2013), 797--822.","journal-title":"The VLDB journal"},{"key":"e_1_2_1_72_1","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","author":"Yuan Ganzhao","year":"2016","unstructured":"Ganzhao Yuan , Yin Yang , Zhenjie Zhang , and Zhifeng Hao . 2016 . Convex Optimization for Linear Query Processing under Approximate Differential Privacy . In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Ganzhao Yuan, Yin Yang, Zhenjie Zhang, and Zhifeng Hao. 2016. Convex Optimization for Linear Query Processing under Approximate Differential Privacy. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350252"},{"key":"e_1_2_1_74_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2699501","article-title":"Optimizing batch linear queries under exact and approximate differential privacy","volume":"40","author":"Yuan Ganzhao","year":"2015","unstructured":"Ganzhao Yuan , Zhenjie Zhang , Marianne Winslett , Xiaokui Xiao , Yin Yang , and Zhifeng Hao . 2015 . Optimizing batch linear queries under exact and approximate differential privacy . ACM Transactions on Database Systems (TODS) 40 , 2 (2015), 1 -- 47 . Ganzhao Yuan, Zhenjie Zhang, Marianne Winslett, Xiaokui Xiao, Yin Yang, and Zhifeng Hao. 2015. Optimizing batch linear queries under exact and approximate differential privacy. ACM Transactions on Database Systems (TODS) 40, 2 (2015), 1--47.","journal-title":"ACM Transactions on Database Systems (TODS)"},{"key":"e_1_2_1_75_1","article-title":"PrivBayes: Private Data Release via Bayesian Networks","volume":"42","author":"Zhang Jun","year":"2017","unstructured":"Jun Zhang , Graham Cormode , Cecilia M. Procopiuc , Divesh Srivastava , and Xiaokui Xiao . 2017 . PrivBayes: Private Data Release via Bayesian Networks . ACM Trans. Database Syst. 42 , 4, Article 25 (oct 2017), 41 pages. Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017. PrivBayes: Private Data Release via Bayesian Networks. ACM Trans. Database Syst. 42, 4, Article 25 (oct 2017), 41 pages.","journal-title":"ACM Trans. Database Syst."},{"key":"e_1_2_1_76_1","volume-title":"Proceedings of the 2014 SIAM international conference on data mining. SIAM, 587--595","author":"Zhang Xiaojian","year":"2014","unstructured":"Xiaojian Zhang , Rui Chen , Jianliang Xu , Xiaofeng Meng , and Yingtao Xie . 2014 . Towards accurate histogram publication under differential privacy . In Proceedings of the 2014 SIAM international conference on data mining. SIAM, 587--595 . Xiaojian Zhang, Rui Chen, Jianliang Xu, Xiaofeng Meng, and Yingtao Xie. 2014. Towards accurate histogram publication under differential privacy. In Proceedings of the 2014 SIAM international conference on data mining. SIAM, 587--595."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3594512.3594519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:36:04Z","timestamp":1687480564000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3594512.3594519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":74,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10.14778\/3594512.3594519"],"URL":"https:\/\/doi.org\/10.14778\/3594512.3594519","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,4]]},"assertion":[{"value":"2023-06-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}