{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:22:46Z","timestamp":1743063766323,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030314996"},{"type":"electronic","value":"9783030315009"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>\nData analysis is expected to provide accurate descriptions of the data. However, this is in opposition to privacy requirements when working with sensitive data. In this case, there is a need to ensure that no disclosure of sensitive information takes place by releasing the data analysis results. Therefore, privacy-preserving data analysis has become significant. Enforcing strict privacy guarantees can significantly distort data or the results of the data analysis, thus limiting their analytical utility (i.e., differential privacy). In an attempt to address this issue, in this paper we discuss how \u201cintegral privacy\u201d; a re-sampling based privacy model; can be used to compute descriptive statistics of a given dataset with high utility. In integral privacy, privacy is achieved through the notion of stability, which leads to release of the least susceptible data analysis result towards the changes in the input dataset. Here, stability is explained by the relative frequency of different generators (re-samples of data) that lead to the same data analysis results. In this work, we compare the results of integrally private statistics with respect to different theoretical data distributions and real world data with differing parameters. Moreover, the results are compared with statistics obtained through differential privacy. Finally, through empirical analysis, it is shown that the integral privacy based approach has high utility and robustness compared to differential privacy. Due to the computational complexity of the method we propose that integral privacy to be more suitable towards small datasets where differential privacy performs poorly. However, adopting an efficient re-sampling mechanism can further improve the computational efficiency in terms of integral privacy.\n\n<\/jats:p>","DOI":"10.1007\/978-3-030-31500-9_2","type":"book-chapter","created":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T23:26:53Z","timestamp":1568935613000},"page":"22-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integral Privacy Compliant Statistics Computation"],"prefix":"10.1007","author":[{"given":"Navoda","family":"Senavirathne","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vicen\u00e7","family":"Torra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"2_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/978-3-319-48965-0_44","volume-title":"Cryptology and Network Security","author":"V Torra","year":"2016","unstructured":"Torra, V., Navarro-Arribas, G.: Integral privacy. In: Foresti, S., Persiano, G. (eds.) CANS 2016. LNCS, vol. 10052, pp. 661\u2013669. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48965-0_44"},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/11681878_14","volume-title":"Theory of Cryptography","author":"C Dwork","year":"2006","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265\u2013284. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11681878_14"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: the SuLQ framework. In: Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 128\u2013138. ACM (2005)","DOI":"10.1145\/1065167.1065184"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Dwork, C., Smith, A.: Differential privacy for statistics: what we know and what we want to learn. J. Priv. Confid. 1(2) (2010)","DOI":"10.29012\/jpc.v1i2.570"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Dwork, C., Lei, J.: Differential privacy and robust statistics. In: STOC, vol. 9, pp. 371\u2013380 (2009)","DOI":"10.1145\/1536414.1536466"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Clifton, C., Tassa, T.: On syntactic anonymity and differential privacy. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 88\u201393. IEEE (2013)","DOI":"10.1109\/ICDEW.2013.6547433"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.cose.2019.01.006","volume":"83","author":"N Senavirathne","year":"2019","unstructured":"Senavirathne, N., Torra, V.: Integrally private model selection for decision trees. Comput. Secur. 83, 167\u2013181 (2019)","journal-title":"Comput. Secur."},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Senavirathne, N., Torra, V.: Approximating robust linear regression with an integral privacy guarantee. In: 2018 16th Annual Conference on Privacy, Security and Trust (PST), pp. 1\u201310. IEEE (2018)","DOI":"10.1109\/PST.2018.8514161"},{"key":"2_CR9","series-title":"Springer Series in Statistics (Perspectives in Statistics)","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/978-1-4612-4380-9_41","volume-title":"Breakthroughs in Statistics","author":"B Efron","year":"1992","unstructured":"Efron, B.: Bootstrap methods: another look at the Jackknife. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics), pp. 569\u2013593. Springer, New York (1992). https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_41"},{"issue":"6","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TIFS.2017.2663337","volume":"12","author":"J Soria-Comas","year":"2017","unstructured":"Soria-Comas, J., Domingo-Ferrer, J., S\u00e1nchez, D., Meg\u00edas, D.: Individual differential privacy: a utility-preserving formulation of differential privacy guarantees. IEEE Trans. Inf. Forensics Secur. 12(6), 1418\u20131429 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"4","key":"2_CR11","first-page":"1","volume":"8","author":"N Li","year":"2016","unstructured":"Li, N., Lyu, M., Dong, S., Yang, W.: Differential privacy: from theory to practice. Synth. Lect. Inf. Secur. Priv. Trust. 8(4), 1\u2013138 (2016)","journal-title":"Synth. Lect. Inf. Secur. Priv. Trust."},{"key":"2_CR12","unstructured":"Dwork, C., Lei, J.: Differential privacy and robust statistics. In: Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, Bethesda, MD, USA, 31 May\u2013June 2 2009, pp 371\u2013380 (2009)"}],"container-title":["Lecture Notes in Computer Science","Data Privacy Management, Cryptocurrencies and Blockchain Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31500-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T17:02:31Z","timestamp":1737133351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31500-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030314996","9783030315009"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31500-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Data Privacy Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dpm2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/esorics2019.uni.lu\/workshops\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}