dataesr/scientific-paragraphs-categorization huggingface dataset:
Contributions. The key contributions of this paper are: (1) We show, analytically and with experiments on real datasets, that the BA mechanism, in addition to geo-ind, provides an elastic distinguishability metric. As such, it protects the privacy of isolated locations, which the standard LAP for geo-ind fails at. (2) We prove that BA produces an invertible mechanism, which means that the MLE is unique. This is crucial to prove that the IBU always converges to the true distribution and that, therefore, we can get a good statistical utility. (3)  We establish a duality between BA and IBU, thus demonstrating a connection between rate-distortion theory and the expectation-maximization method from statistics. (4) We show experimentally that BA provides a better statistical utility than LAP for high levels of privacy, eventually becoming comparable as the level of privacy decreases.
(5) Since the construction of the optimal BA requires precise knowledge of the true distribution, we propose an iterative method (PRIVIC) that alternates between BA and IBU, thus getting a better and better estimation of the true distribution as more (noisy) data get collected. We show, both formally and with experiments on real location datasets, that PRIVIC converges to the true distribution. In summary, PRIVIC produces a geo-indistinguishable LPPM with an elastic distinguishability metric, which optimizes the trade-off with the QoS and provides high statistical utility. (6)  We investigate the effect on the privacy guarantees of our method by considering adversarial users who report their locations falsely to compromise the privacy of the isolated locations in the map. Related Work. The trade-off between privacy and utility has been widely studied in the literature  [11, 36].
Optimization techniques for DP and utility for statistical databases have been analyzed by the community from various perspectives  [30, 31, 40]. There have been works focusing on devising privacy mechanisms that are optimal to limit the privacy risk against Bayesian inference attacks while maximizing the utility  [47, 49]. In  [42] , Oya et al. examine an optimal LPPM w. r. t. various privacy and utility metrics for the user. In  [43] , Oya et al. consider the optimal LPPM proposed by Shokri et al. in  [49]  which maximizes a notion of privacy (the adversarial error) under some bound on the QoS. The construction of the optimal LPPM requires the knowledge of the original distribution, and  [43]  uses the EM method to estimate it and design blank-slate models empirically shown to outperform the traditional hardwired models.
Plan of the paper. Section 2 introduces preliminary ideas from the literature relevant to this work. Section 3 highlights BA as an LPPM because of its extensive privacy-preserving properties. Section 4 establishes the duality between BA and IBU. Section 5 explains our proposed method (PRIVIC). Section 6 exhibits the working of PRIVIC with experiments using real locations from the Gowalla dataset illustrating the convergence of our method. Section 7 discusses and illustrates with experiments the vulnerability of PRIVIC under adversarial data submission and Section 8 concludes. Appendices A and B contain the proofs of the theorems derived in the paper and the relevant tables supporting the experimental analysis of PRIVIC, respectively. The code used for implementing our mechanism for experiments is available at https://anonymous.
We will functionally denote πX as IBU(𝒒, C). Next, we recall a generalization of IBU from the literature that we use in this work. Generalized IBU (GIBU)  [26]  applies IBU in parallel to several empirical distributions derived from the application of (possibly different) obfuscation mechanisms to various sets of samples from the same distribution. Definition 2. 6 (Generalized iterative Bayesian update  [26] ). Let 𝒙 (1) ,. , 𝒙 (𝑁 ) , with 𝒙 (𝑡 ) = (𝑥 (𝑡 ) 1 ,. , 𝑥 (𝑡 ) 𝑛 ) for every 𝑡 ∈ {1,. , 𝑁 }, be 𝑁 datasets s. t. the entries 𝑥 (𝑡 ) 𝑖 for each 𝑖 ∈ {1,. , 𝑛} are i. i. d. samples from the discrete space X following the probability distribution 𝜋 X. Let C (1) ,. , C (𝑁 ) be 𝑁 privacy mechanisms that locally obfuscate points from X to Y such that the mechanism C (𝑡 ) is applied to the dataset 𝒙 (𝑡 ) and C Let G = C (1).
We perform experiments using real location data from the Gowalla dataset  [19, 39]. We consider 10,078 Gowalla check-ins from a central part of Paris bounded by latitudes  (48. 8286, 48. 8798 ) and longitudes (2. 2855, 2. 3909) covering an area of 8Km×6Km discretized with a 16 × 12 grid. In order to demonstrate the property of an elastic mechanism, we artificially introduced an "island" amidst the locations in Paris by choosing a grid 𝐴 in a low-density area of the dataset (in the southwest region), assigning the probability mass of the grids around 𝐴 to 0, and dumping this cumulative mass from the surrounding region to 𝐴, ensuring that the sum of the probability masses of all the grids remains to be 1. We call 𝐴 as a vulnerable location in the map as it is isolated from the crowded area. To visualize the elastic behaviour of the mechanisms for locations in crowded regions, we consider another grid 𝐵 in the central part of the map which has a high probability mass and has a highly populated surroundingwe refer to such a grid 𝐵 as a strong location in the map.
Statistical utility: BA vs LAP Now we proceed to empirically compare the statistical utility of BA and LAP by performing experiments on the locations obtained from the Gowalla dataset for two different cities: Paris and San Francisco. In addition to the same setting for the Gowalla check-ins in Paris as considered in the experiments of Section 3. 1, here we also test for 123,025 check-in locations from the Gowalla dataset in a northern part of San Francisco bounded by latitudes (37. 7228, 37. 7946) and longitudes (-122. 5153, -122. 3789) covering an area of 12Km×8Km discretized with a 24×17 grid. The locations were privatized with BA and LAP under varying levels of privacy -the loss parameter, 𝛽, for BA ranged from 0. 2 to 5. 0, which implies that the value of the geo-ind parameter, 𝜖, ranged from 0.
4 (very high level of privacy) to 10. 0 (almost no privacy). To account for the randomness in the process of generating the sanitized locations, 5 simulations were run for each value of the privacy parameter for obfuscating every location in both datasets. Figure  3  reveals that BA possesses a significantly better statistical utility than LAP for a high level of privacy (for 𝛽 ∈ (0. 4, 1. 4] and 𝛽 ∈ (0, 1), i. e. , 𝜖 up to 2. 8 and 2, in Paris and San Francisco datasets, respectively). As the level of privacy decreases, the EMD of BA becomes worse than that of LAP. We conjecture that this is the price to pay for the added privacy provided by the elasticity of the mechanism. Eventually, the EMD between the true and the estimated PMFs converge to 0 in both mechanisms, as we would expect, fostering the maximum possible statistical utility with, practically, no privacy guarantee.
EXPERIMENTAL ANALYSIS OF PRIVIC In this section, we describe the empirical results obtained by carrying out experiments to illustrate and validate the working of our proposed method. Standard Python packages were used to run the experiments in a MacOS Ventura 13. 2. 1 environment with an Intel core i9 processor and 32 GB of RAM. Like in the previous experiments to compare the statistical utilities of BA and LAP, as elaborated in Section 3. 2, we use real locations from the same regions in Paris and San Francisco from the Gowalla dataset  [19, 39]. In particular, we consider Gowalla check-ins from (i) a northern part of San Francisco bounded by latitudes  (37. 7228, 37. We implemented PRIVIC on the locations from Paris and San Francisco separately to judge its performance on real data with very different priors.
In both cases, we ran our mechanism until it empirically converged. 15 cycles of PRIVIC were required for the Paris dataset where each cycle comprised 8 iterations of BA until it converged to generate the privacy mechanism and 10 iterations of IBU until it converged to the MLE of the prior. For the San Francisco dataset, PRIVIC needed 8 cycles to converge with 5 iterations of BA and IBU each to converge in every cycle. The complexities and the run-times of BA and IBU are summarised in Table  2. In both cases, we assigned the value of the loss parameter signifying the QoS of the users, 𝛽, to be 0. 5 and 1. This was done to test the performance of PRIVIC in estimating the true PMF under two different levels of privacy. Each experiment was run for 5 rounds of simulation to calibrate the randomness of the sampling and obfuscation.
However, as we would anticipate, the statistical utility for 𝛽 = 1 is better than that for 𝛽 = 0. 5. Now we shift our attention to analyze the performance of PRIVIC in preserving the statistical utility and its long-term behaviour of the two datasets. Figure  8  shows us the EMD between the true distribution of the locations in Paris and its estimate by IBU under PRIVIC in each of its 15 cycles under the two settings of privacy (𝛽 = 0. 5, 1). One of the most crucial observations here is that the EMD between the true and the estimated PMFs seems to decrease with the number of iterations and it finally converges, implying that the estimation of PMFs given by PRIVIC seems to improve at the end of each cycle and, eventually, it converges to the MLE of the prior of the noisy locations, giving the estimate of the true PMF.
This, empirically, suggests the convergence of the entire method. This is a major difference from the work of  [43]  which, as we pointed out before, has the potential of encountering an LPPM which is optimal according to the standards set by Shokri et al. in  [48]  but     the EM method used to estimate the true distribution would fail to converge for that mechanism as illustrated in Example 5. 1. We observe a very similar trend for the San Francisco dataset. Figure  9  shows the statistical utility of the mechanism generated by PRIVIC under each of its 8 cycles for 𝛽 = 0. 5 and 𝛽 = 1. The explicit values of the EMD between the true and the estimated PMFs on the location data from Paris and San Francisco for both the settings of the loss parameter can be found in Tables  3  and 4  in Appendix B.
In the next part of the experiments, we set ourselves to dissect the trend of the statistical utility harboured by PRIVIC w. r. t. the level of geo-ind it guarantees. We recall that the higher the value of 𝛽, the lesser the local noise that is injected into the data, and, hence, the worse will be the statistical utility, staying consistent with our observations in Figure  7. We continue working with the location data from Paris and San Francisco obtained from the Gowalla dataset in the same framework as described before. We consider 𝛽 taking the values 0. 1, 0. 3, 0. 5, 0. 7, 0. 9, 1, and for each value of the loss parameter, we run PRIVIC on both datasets using the same number of iterations as in the previous experiments. We adhere to 5 rounds of simulation for each 𝛽 to account for the randomness generated in the obfuscation process.
VULNERABILITY OF PRIVIC In this section, we illustrate a potential vulnerability of PRIVIC when a subset of colluding users (adversarial users) intentionally deviate from the correct use of the protocol. The attack consists in falsely reporting their location in order to alter the estimation of the true distribution and, consequently, the obfuscation mechanism produced by BA. Specifically, we study two cases: (i) adversaries reporting a crowded location (strong location) and adversaries reporting an isolated location (vulnerable location). We used the real locations from the Paris dataset with the geospatially isolated "island" (as illustrated by Figure  1  presented in Section 3) representing a strong and a vulnerable location in the map denoted by points A and B in the figure, respectively.
Moreover, PRIVIC efficiently estimates the MLE of the distribution of the original data and, thus, yields a high statistical utility for the service providers. Finally, we have illustrated the convergence and the general functioning of PRIVIC with experiments on real location datasets. We believe that our results can be extended easily to other kinds of data, including those with high dimensions, and to other notions of distortion measures since the analysis carried out in this paper does not depend on the notion of distance used. '.
Variables need to be examined at multiple time points; profile plots for time-varying variables can be helpful to summarize changes and variability of variables within participants as well as possible longitudinal trends across participants. Longitudinal trends, correlations and variability, and period or cohort effects may also be examined. In the longitudinal setting IDA explorations can quickly become overwhelming even with a small numbers of variables. Our IDA checklist may be useful to guide researchers to carefully consider topics. A worked example with available data and reproducible R code including many effective data visualizations is provided, which can be adapted for developing IDA plans for other studies. We believe that the checklist, the worked example and the R code will help data analysts in planning and performing IDA data screening for longitudinal studies to provide researchers with a better understanding of their dataset.
Acknowledgments This work was developed as part of the international initiative of  Strengthening Analytical Thinking for Observational Studies (STRATOS ). The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies (http://stratos-initiative. org/). Members of the Topic Group Initial Data Analysis of the STRATOS Initiative are  Mark Baillie  ( Switzerland ),  Marianne Huebner  ( USA ),  Saskia le Cessie  ( Netherlands ),  Lara Lusa  ( Slovenia ),  Carsten Oliver Schmidt  (Germany). L. L. was partially supported by  ARRS research program   P3-0154.
The percentage cost-reduction (PCR) obtained by a target method with respect to any other approach is computed as: P CR = Co -Ct Co • 100, (29) where C t is the cost obtained with the target method and C o is the cost of the other approach, so that it is positive when the target method achieves lower costs than the other one. 2) Stochastic Execution Profiles: To evaluate our STS method, we had to model the probability distributions for the number of epochs required to complete the training of a Deep Learning job. For this purpose, we collected data on the execution of DL tasks of various kinds using different neural networks and AI frameworks. We considered a subset of the ImageNet competition dataset  [37] , including 10 classes with 1300 images each. We trained jobs exploiting ResNet  [38] , VGG16  [39] , AlexNet  [40]  and MobileNetV2  [41] , varying the batch size  (16, 32, 64)  and the optimizer (Adam, SGD).
Tool: B-pomset encoder We developed a companion tool to simulate BPs and analyse their applications to choreographies. The tool is opensource (https://github. com /arcalab /choreo /tree /b -pomset), developed in Scala and compiled into JavaScript (JS). A snapshot of a compiled JS can be executed from an internet browser at http://lmf. di. uminho. pt /b -pomset/. The tool fulfils 3 main objectives: (1) internal validation, providing the authors early insights over alternative notions of well-formedness, (2) dissemination, to better explain the propose analysis using an interactive environment, and (3) algorithmic insights of well-formedness (cf. Section 5. 2), by transforming the declarative definitions into concrete algorithms and identifying possible bottlenecks. This section focuses on the last two objectives, i.
Acknowledgements We thank the anonymous reviewers for their many insightful remarks, as well as for their advices on structuring the presentation of our contributions. Funding  Sung-Shik Jongmans : Netherlands Organisation of Scientific Research: 016. Veni. 192. 103. José Proenc¸a: This work was partially supported by  National Funds  through  FCT/MCTES (Portuguese Foundation for Science and Technology) , within the  CISTER Unit  ( UIDP/UIDB/04234/2020 ) and the  IBEX  project ( PTDC/CCI-COM/4280/2021 ); by the  EU/Next Generation , within the  Recovery and Resilience Plan , within project  Route 25  ( TRB/2022/00061 -C645463824-00000063 ); and by national funds through  FCT  and European funds through  EU ECSEL JU , within project  VALU3S  ( ECSEL/0016/2019 -JU  grant nr. 876852 ). Ilaria Castellani : This work was partially funded by the  ANR17-CE25-0014-01   CISC  project.
Acknowledgments This work was supported by a public grant as part of the  Investissement d'avenir  project, reference  ANR-11-LABX-0056-LMH ,  LabEx  LMH. This work was also supported by  Guangdong Basic and Applied Basic Research Foundation  (Grant No. 2019A1515110177 ),  Guangdong Provincial Key Laboratory  (Grant No. 2020B121201001 ), the  Program for Guangdong Introducing Innovative and Enterpreneurial Teams  (Grant No. 2017ZT07X386 ),  Shenzhen Science and Technology Program  (Grant No. KQTD2016112514355531 ).
Acknowledgements. The research leading to this work was carried out as a part of the  Smart4RES  project (European Union's  Horizon 2020 , No. 864 337 ). The sole responsibility of this publication lies with the authors. The  European Union  is not responsible for any use that may be made of the information contained therein. We acknowledge  Météo-France  and the  Occitanie Région  for funding the PhD of  Marie-Adèle Magnaldo. We also thank  Yves-Marie Saint-Drenan ,  Yves Bouteloup ,  Yann Seity ,  Marie Cassas  and  Emmanuel Fontaine  for the fruitful discussions and/or the technical help.
Data availability. The cloud satellite products developed by the NWC Eumetsat SAF are available on this site: https://www. icare. univ-lille. fr/ and the documentation on: http://www. nwcsaf. org. The satellite product from Copernicus Atmosphere Monitoring Service (CAMS) are available on: https://atmosphere. copernicus. eu/. The data are freely available for research purposes at this address: https://donneespubliques. meteofrance. fr/?fond=produit&id_produit=298&id_rubrique=32. Access to the AROME code can be requested via the ACCORD consortium webpage: http://www. umr-cnrm. fr/accord/. AROME forecast are available on: https://doi. org/10. 5281/zenodo. 7928622. As the data set used for this study is very large (800Go), only the shortwave downward radiation is available. The others parameters can be made available on request from the corresponding author.
Third part: reference stride annotation with DTW The pattern isolated in the previous section corresponds to a complete stride, probably centered on the SwP. The next step is to annotate this reference stride with the best estimates of TO and HS. To do this, a model stride was used to annotate each reference stride using DTW. Model Stride. We extracted a "model stride" from our dataset of healthy subjects, which has all 4 stride events annotated. Specifically, TO and HS were annotated with the GR, while FC and IC were annotated with their respective events. FF and HO were estimated and are provided as an indication. The signals of interest corresponding to this stride are illustrated in Figure  5. DTW. The Dynamic Time Warping (DTW) algorithm is a distance measure that aligns and transforms two time series through a non-linear transformation to obtain an optimal match between them.
To validate our proposal, we conduct a comprehensive and extensive set of experiments on four real-world open datasets. Under the same privacy guarantee, results show that ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates. The remainder of this paper is organized as follows. In Section 2, we review the privacy notion in consideration, i. e. , LDP and the protocols. In Section 3, we extend the analysis of GRR, OUE, and SUE to multidimensional data collections. In Section 4 we present the memoization-based framework for longitudinal data collections, the extension and analysis of longitudinal GRR and the longitudinal UE-based protocols and the numerical evaluation of their performance, and we present our ALLOMFREE solution.
LDP Local differential privacy, initially formalized in  [10] , protects an individual's privacy during the data collection process. A formal definition of LDP is given as follows: Definition 1 ( -Local Differential Privacy). A randomized algorithm A satisfies -LDP if, for any pair of input values v 1 , v 2 ∈ Domain(A) and any possible output y of A: Pr[A(v 1 ) = y] ≤ e • Pr[A(v 2 ) = y] Similar to the centralized model of DP, LDP also enjoys several important properties, e. g. , immunity to post-processing (F(A) is -LDP for any function F) and composability  [3]. That is, combining the results from d locally differentially private protocols also satisfy LDP. If these protocols are applied separately in disjointed subsets of the dataset, = max( 1 -,. , d )-LDP (parallel composition). On the other hand, if these protocols are sequentially applied to the same dataset, = d i=1 i -LDP (sequential composition).
Setup of experiments The main goal of our experiments is to evaluate the proposed longitudinal LDP protocols on multidimensional frequency estimates a single time, i. e. , satisfying 1 -LDP (as in  [11, 40, 39] , for example). Environment. All algorithms are implemented in Python 3. 8. 8 with NumPy 1. 19. 5 and Numba 0. 53. 1 libraries. The codes we develop and use for all experiments are available in a Github repository  1. In all experiments, we report average results over 100 runs as LDP algorithms are randomized. Methods evaluated. We consider for evaluation the following solutions and protocols: • Solution Smp (cf. Section 3), which randomly samples a single attribute to be sent with the whole privacy budget. We will experiment with the state-of-the-art protocols, namely, L-SUE and L-OUE, and with our extended protocols L-OSUE and L-SOUE; • Our ALLOMFREE solution (cf.
More precisely, MS E avg = 1 τ t∈[1,τ] 1 d j∈[1,d] 1 |A j | v∈A j ( f (v i ) -f (v i )) 2 Datasets. For the ease of reproducibility, we conduct our experiments on four multidimensional open datasets. Algorithm 1 User-side algorithm of ALLOMFREE. 1: Input : v = [v 1 , v 2 ,. , v d ], k = [k 1 , k 2 ,. , k d ], A = {L-GRR, L-OSUE}, ∞ , f p L-GRR ← p 1 = e ∞ e ∞ +k r -1 , q 1 = 1-p 1 k r -1 , p 2 = e 1 + ∞ -1 -k r e 1 +(k r -1)e ∞ +e 1 +e 1 + ∞ -1 , q 2 = 1-p 2 k r -1 Get p 2 and q 2 with Eq. (  12 ) 5: f p L-OS UE ← p 1 = 1 2 , q 1 = 1 e ∞ +1 , p 2 = 1-e 1 + ∞ e 1 -e ∞ -e 1 + ∞ +1 , q 2 = 1p 2 Get p 2 and q 2 with Eq. (  14 ) 6: if Var * [ fL (L-GRR) ]( f p L-GRR ) ≤ Var * [ fL (L-OSUE) ]( f p L-OS UE ) : Check variances with Eq. (11)  7: A ← L-GRR Select L-GRR as local randomizer 8: else 9: A ← L-OSUE Select L-OSUE as local randomizer 10: B ← A(B, p 1 , q 1 , k r ) First round of sanitization (permanent memoization) 11: for t ∈ [1, τ] do 12: B = A(B , p 2 , q 2 , k r ) Second round of sanitization 13: end for 14: send : (t, r, B ) for t ∈  [1, τ]  • Nursery.
A dataset from the UCI machine learning repository  [46]  with d = 9 categorical attributes and n = 12960 samples. The domain size of each attribute is k =  [3, 5, 4, 4, 3, 2, 3, 3, 5] , respectively. • Adult. A dataset from the UCI machine learning repository  [46]  with d = 9 categorical attributes and n = 45222 samples after cleaning the data. The domain size of each attribute is k = [7, 16, • MS-FIMU. An open dataset from  [47]  with d = 6 categorical attributes and n = 88935 samples. The domain size of each attribute is k =  [3, 3, 8, 12, 37, 11] , respectively. • Census-Income. A dataset from the UCI machine learning repository  [46]  with d = 33 categorical attributes and n = 299285 samples. The domain size of each attribute is k =  [9, 52, 47, 17, 3,. , 43, 43, 43, 5, 3, 3, 3, 2] , respectively.