3 Search Results for "Stausholm, Nina Mesing"

Document
DOI: 10.4230/LIPIcs.FORC.2025.12
Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High ε Regime

Authors: Charlie Harrison and Pasin Manurangsi

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
Differential privacy (DP) can be achieved in a distributed manner, where multiple parties add independent noise such that their sum protects the overall dataset with DP. A common technique here is for each party to sample their noise from the decomposition of an infinitely divisible distribution. We analyze two mechanisms in this setting: 1) the generalized discrete Laplace (GDL) mechanism, whose distribution (which is closed under summation) follows from differences of i.i.d. negative binomial shares, and 2) the multi-scale discrete Laplace (MSDLap) mechanism, a novel mechanism following the sum of multiple i.i.d. discrete Laplace shares at different scales. For ε ≥ 1, our mechanisms can be parameterized to have O(Δ³ e^{-ε}) and O(min(Δ³ e^{-ε}, Δ² e^{-2ε/3})) MSE, respectively, where Δ denote the sensitivity; the latter bound matches known optimality results. Furthermore, the MSDLap mechanism has the optimal MSE including constants as ε → ∞. We also show a transformation from the discrete setting to the continuous setting, which allows us to transform both mechanisms to the continuous setting and thereby achieve the optimal O(Δ² e^{-2ε / 3}) MSE. To our knowledge, these are the first infinitely divisible additive noise mechanisms that achieve order-optimal MSE under pure DP for either the discrete or continuous setting, so our work shows formally there is no separation in utility when query-independent noise adding mechanisms are restricted to infinitely divisible noise. For the continuous setting, our result improves upon Pagh and Stausholm’s Arete distribution which gives an MSE of O(Δ² e^{-ε/4}) [Pagh and Stausholm, 2022]. Furthermore, we give an exact sampler tuned to efficiently implement the MSDLap mechanism, and we apply our results to improve a state of the art multi-message shuffle DP protocol from [Balle et al., 2020] in the high ε regime.
Cite as

Charlie Harrison and Pasin Manurangsi. Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High ε Regime. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 12:1-12:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{harrison_et_al:LIPIcs.FORC.2025.12,
  author =	{Harrison, Charlie and Manurangsi, Pasin},
  title =	{{Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High \epsilon Regime}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{12:1--12:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.12},
  URN =		{urn:nbn:de:0030-drops-231396},
  doi =		{10.4230/LIPIcs.FORC.2025.12},
  annote =	{Keywords: Differential Privacy, Distributed Noise Addition}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2021.18
Efficient Differentially Private F₀ Linear Sketching

Authors: Rasmus Pagh and Nina Mesing Stausholm

Published in: LIPIcs, Volume 186, 24th International Conference on Database Theory (ICDT 2021)

Abstract
A powerful feature of linear sketches is that from sketches of two data vectors, one can compute the sketch of the difference between the vectors. This allows us to answer fine-grained questions about the difference between two data sets. In this work we consider how to construct sketches for weighted F₀, i.e., the summed weights of the elements in the data set, that are small, differentially private, and computationally efficient. Let a weight vector w ∈ (0,1]^u be given. For x ∈ {0,1}^u we are interested in estimating ||x∘w||₁ where ∘ is the Hadamard product (entrywise product). Building on a technique of Kushilevitz et al. (STOC 1998), we introduce a sketch (depending on w) that is linear over GF(2), mapping a vector x ∈ {0,1}^u to Hx ∈ {0,1}^τ for a matrix H sampled from a suitable distribution ℋ. Differential privacy is achieved by using randomized response, flipping each bit of Hx with probability p < 1/2. That is, for a vector φ ∈ {0,1}^τ where Pr[(φ)_j = 1] = p independently for each entry j, we consider the noisy sketch Hx + φ, where the addition of noise happens over GF(2). We show that for every choice of 0 < β < 1 and ε = O(1) there exists p < 1/2 and a distribution ℋ of linear sketches of size τ = O(log²(u)ε^{-2}β^{-2}) such that: 1) For random H∼ℋ and noise vector φ, given Hx + φ we can compute an estimate of ||x∘w||₁ that is accurate within a factor 1±β, plus additive error O(log(u)ε^{-2}β^{-2}), w. p. 1-u^{-1}, and 2) For every H∼ℋ, Hx + φ is ε-differentially private over the randomness in φ. The special case w = (1,… ,1) is unweighted F₀. Previously, Mir et al. (PODS 2011) and Kenthapadi et al. (J. Priv. Confidentiality 2013) had described a differentially private way of sketching unweighted F₀, but the algorithms for calibrating noise to their sketches are not computationally efficient, either using quasipolynomial time in the sketch size or superlinear time in the universe size u. For fixed ε the size of our sketch is polynomially related to the lower bound of Ω(log(u)β^{-2}) bits by Jayram & Woodruff (Trans. Algorithms 2013). The additive error is comparable to the bound of Ω(1/ε) of Hardt & Talwar (STOC 2010). An application of our sketch is that two sketches can be added to form a noisy sketch of the form H(x₁+x₂) + (φ₁+φ₂), which allows us to estimate ||(x₁+x₂)∘w||₁. Since addition is over GF(2), this is the weight of the symmetric difference of the vectors x₁ and x₂. Recent work has shown how to privately and efficiently compute an estimate for the symmetric difference size of two sets using (non-linear) sketches such as FM-sketches and Bloom Filters, but these methods have an error bound no better than O(√{̄{m}}), where ̄{m} is an upper bound on ||x₁||₀ and ||x₂||₀. This improves previous work when β = o (1/√{̄{m}}) and log(u)/ε = ̄{m}^{o(1)}. In conclusion our results both improve the efficiency of existing methods for unweighted F₀ estimation and extend to a weighted generalization. We also give a distributed streaming implementation for estimating the size of the union between two input streams.
Cite as

Rasmus Pagh and Nina Mesing Stausholm. Efficient Differentially Private F₀ Linear Sketching. In 24th International Conference on Database Theory (ICDT 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 186, pp. 18:1-18:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{pagh_et_al:LIPIcs.ICDT.2021.18,
  author =	{Pagh, Rasmus and Stausholm, Nina Mesing},
  title =	{{Efficient Differentially Private F₀ Linear Sketching}},
  booktitle =	{24th International Conference on Database Theory (ICDT 2021)},
  pages =	{18:1--18:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-179-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{186},
  editor =	{Yi, Ke and Wei, Zhewei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2021.18},
  URN =		{urn:nbn:de:0030-drops-137264},
  doi =		{10.4230/LIPIcs.ICDT.2021.18},
  annote =	{Keywords: Differential Privacy, Linear Sketches, Weighted F0 Estimation}
}
Document
DOI: 10.4230/LIPIcs.ESA.2019.74
Hardness of Bichromatic Closest Pair with Jaccard Similarity

Authors: Rasmus Pagh, Nina Mesing Stausholm, and Mikkel Thorup

Published in: LIPIcs, Volume 144, 27th Annual European Symposium on Algorithms (ESA 2019)

Abstract
Consider collections A and B of red and blue sets, respectively. Bichromatic Closest Pair is the problem of finding a pair from A x B that has similarity higher than a given threshold according to some similarity measure. Our focus here is the classic Jaccard similarity |a cap b|/|a cup b| for (a,b) in A x B. We consider the approximate version of the problem where we are given thresholds j_1 > j_2 and wish to return a pair from A x B that has Jaccard similarity higher than j_2 if there exists a pair in A x B with Jaccard similarity at least j_1. The classic locality sensitive hashing (LSH) algorithm of Indyk and Motwani (STOC '98), instantiated with the MinHash LSH function of Broder et al., solves this problem in Õ(n^(2-delta)) time if j_1 >= j_2^(1-delta). In particular, for delta=Omega(1), the approximation ratio j_1/j_2 = 1/j_2^delta increases polynomially in 1/j_2. In this paper we give a corresponding hardness result. Assuming the Orthogonal Vectors Conjecture (OVC), we show that there cannot be a general solution that solves the Bichromatic Closest Pair problem in O(n^(2-Omega(1))) time for j_1/j_2 = 1/j_2^o(1). Specifically, assuming OVC, we prove that for any delta>0 there exists an epsilon>0 such that Bichromatic Closest Pair with Jaccard similarity requires time Omega(n^(2-delta)) for any choice of thresholds j_2 < j_1 < 1-delta, that satisfy j_1 <= j_2^(1-epsilon).
Cite as

Rasmus Pagh, Nina Mesing Stausholm, and Mikkel Thorup. Hardness of Bichromatic Closest Pair with Jaccard Similarity. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 74:1-74:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

Copy BibTex To Clipboard

@InProceedings{pagh_et_al:LIPIcs.ESA.2019.74,
  author =	{Pagh, Rasmus and Stausholm, Nina Mesing and Thorup, Mikkel},
  title =	{{Hardness of Bichromatic Closest Pair with Jaccard Similarity}},
  booktitle =	{27th Annual European Symposium on Algorithms (ESA 2019)},
  pages =	{74:1--74:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-124-5},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{144},
  editor =	{Bender, Michael A. and Svensson, Ola and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2019.74},
  URN =		{urn:nbn:de:0030-drops-111951},
  doi =		{10.4230/LIPIcs.ESA.2019.74},
  annote =	{Keywords: fine-grained complexity, set similarity search, bichromatic closest pair, jaccard similarity}
}
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