Transaction

7fb3e00b2b4933589ddec00247a4eba997b39e93e7cb7b506f56c6a6dcc9b9cf
Timestamp (utc)
2024-07-19 06:32:35
Fee Paid
0.00000006 BSV
(
0.00318446 BSV
-
0.00318440 BSV
)
Fee Rate
2.53 sat/KB
Version
1
Confirmations
76,917
Size Stats
2,371 B

3 Outputs

Total Output:
0.00318440 BSV
  • jmetaB028c97758ff603b29768b74e12acca9e97f6a4e0e83c8a9c1f3c2fa7841f0b8ee5@104e04f4dc7bbb58b675a0be8ec8a2392cd828cadc0c1b85347e2d4ab003150erss.item metarss.netM³<item> <title>Private Mean Estimation with Person-Level Differential Privacy</title> <link>https://arxiv.org/abs/2405.20405</link> <description>arXiv:2405.20405v2 Announce Type: replace-cross Abstract: We study person-level differentially private (DP) mean estimation in the case where each person holds multiple samples. DP here requires the usual notion of distributional stability when $\textit{all}$ of a person's datapoints can be modified. Informally, if $n$ people each have $m$ samples from an unknown $d$-dimensional distribution with bounded $k$-th moments, we show that people are necessary and sufficient to estimate the mean up to distance $\alpha$ in $\ell_2$-norm under $\varepsilon$-differential privacy (and its common relaxations). In the multivariate setting, we give computationally efficient algorithms under approximate-DP and computationally inefficient algorithms under pure DP, and our nearly matching lower bounds hold for the most permissive case of approximate DP. Our computationally efficient estimators are based on the standard clip-and-noise framework, but the analysis for our setting requires both new algorithmic techniques and new analyses. In particular, our new bounds on the tails of sums of independent, vector-valued, bounded-moments random variables may be of interest. \[n = \tilde \Theta\left(\frac{d}{\alpha^2 m} + \frac{d}{\alpha m^{1/2} \varepsilon} + \frac{d}{\alpha^{k/(k-1)} m \varepsilon} + \frac{d}{\varepsilon}\right)\]</description> <guid isPermaLink="false">oai:arXiv.org:2405.20405v2</guid> <category>cs.DS</category> <category>cs.CR</category> <category>cs.IT</category> <category>cs.LG</category> <category>math.IT</category> <category>stat.ML</category> <arxiv:announce_type>replace-cross</arxiv:announce_type> <dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights> <dc:creator>Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan Ullman</dc:creator> </item>
    https://whatsonchain.com/tx/7fb3e00b2b4933589ddec00247a4eba997b39e93e7cb7b506f56c6a6dcc9b9cf