Differentially-Private Multi-Party Sketching for Large-Scale Statistics
We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the ca...
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| Veröffentlicht in: | Proceedings on Privacy Enhancing Technologies Jg. 2020; H. 3; S. 153 - 174 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
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01.07.2020
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| ISSN: | 2299-0984, 2299-0984 |
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| Abstract | We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size.
We begin with the well-known LogLog sketch for computing the number of unique elements in a data stream. We show that this algorithm already achieves differential privacy (even without adding any noise) when computed using a private hash function by a trusted curator. Next, we show how to eliminate this requirement of a private hash function by injecting a small amount of noise, allowing us to instantiate an efficient LogLog protocol for the multi-party setting. To demonstrate the practicality of this approach, we run extensive experimentation on multiple data sets, including the publicly available IP address data set from University of Michigan’s scans of internet IPv4 space, to determine the trade-offs among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes.
Finally, we generalize our approach for the LogLog sketch and obtain a general framework for constructing multi-party differentially private protocols for several other sketching algorithms. |
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| AbstractList | We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size.
We begin with the well-known LogLog sketch for computing the number of unique elements in a data stream. We show that this algorithm already achieves differential privacy (even without adding any noise) when computed using a private hash function by a trusted curator. Next, we show how to eliminate this requirement of a private hash function by injecting a small amount of noise, allowing us to instantiate an efficient LogLog protocol for the multi-party setting. To demonstrate the practicality of this approach, we run extensive experimentation on multiple data sets, including the publicly available IP address data set from University of Michigan’s scans of internet IPv4 space, to determine the trade-offs among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes.
Finally, we generalize our approach for the LogLog sketch and obtain a general framework for constructing multi-party differentially private protocols for several other sketching algorithms. |
| Author | Yerukhimovich, Arkady Kulkarni, Mukul Choi, Seung Geol Dachman-soled, Dana |
| Author_xml | – sequence: 1 givenname: Seung Geol surname: Choi fullname: Choi, Seung Geol email: choi@usna.edu organization: United States Naval Academy – sequence: 2 givenname: Dana surname: Dachman-soled fullname: Dachman-soled, Dana email: danadach@ece.umd.edu organization: University of Maryland, Colleage Park – sequence: 3 givenname: Mukul surname: Kulkarni fullname: Kulkarni, Mukul email: mukul@cs.umass.edu organization: University of MassachusettsAmherst – sequence: 4 givenname: Arkady surname: Yerukhimovich fullname: Yerukhimovich, Arkady email: arkady@gwu.edu organization: George Washington University |
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| Cites_doi | 10.1109/SP40000.2020.00016 10.1145/380752.380850 10.1109/ICDE.2006.44 10.1137/060673096 10.1145/3319535.3345650 10.46298/dmtcs.3545 10.1145/1147954.1147955 10.1137/1.9781611973075.92 10.1007/978-3-540-30576-7_17 10.1145/1340771.1340773 10.1201/9781420010756 10.1007/978-3-319-19962-7_24 10.1109/FOCS.2006.37 10.1145/2452376.2452456 10.1007/978-3-662-43936-4_26 10.1145/28395.28420 10.1109/FOCS.2018.00058 10.1109/FOCS.2012.67 10.1145/2504730.2504755 10.1007/11761679_29 10.1090/conm/026/737400 10.1145/2488608.2488652 10.1016/0022-0000(85)90041-8 10.14778/3291264.3291274 10.1109/TDSC.2015.2423675 10.2478/popets-2019-0018 10.1109/TIFS.2017.2721360 10.1145/1159892.1159900 10.1145/1007352.1007372 10.1145/3146549 10.1515/popets-2018-0010 10.1137/130949117 10.1007/978-3-540-85174-5_25 10.1145/1060590.1060621 10.1561/0400000042 10.1561/3300000019 10.1145/2902251.2902291 10.1145/237814.237827 10.1016/S0304-3975(03)00400-6 10.1006/jcss.1997.1545 10.1145/276698.276876 10.1145/3133956.3134034 10.1109/FOCS.2007.66 10.1137/08074489X 10.1007/978-3-540-39658-1_55 10.1109/FOCS.2008.27 10.1007/978-3-642-03356-8_8 10.1145/2976749.2978310 10.1016/j.jalgor.2003.12.001 10.1007/11681878_14 10.14722/ndss.2016.23175 10.21236/ADA465464 10.1007/s00211-010-0331-6 10.1007/11841036_16 10.1016/j.comnet.2013.05.011 10.1007/3-540-45726-7_1 |
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| SubjectTerms | differential privacy secure computation sketching algorithms |
| Title | Differentially-Private Multi-Party Sketching for Large-Scale Statistics |
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