dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, the geoindistinguishability framework  [2, 14]  has the appealing features of providing formal privacy guarantees independent from the user's prior, being robust with respect to combination attacks, and offering a good trade off between privacy and utility. These features are inherited from the framework of differential privacy  [13]  which prevents an adversary from distinguishing datasets that are "close" based on the Hamming metric. Geoindistinguishability follows the same principles while using the Euclidean distance between locations. Formally, a mechanism provides geo-indistinguishability if the user's real location x is indistinguishable from other nearby locations x , meaning that the mechanism should report any noisy location z with a probability similar to that of reporting the same z when the real location is x.
A solution (commonly employed for various privacy purposes) consist in building a "fence" around sensitive locations so that all points inside are completely indistinguishable from each other. In this way the attacker will be able, after many iterations, to identify the fence but not the exact location inside the fence. We show that such a solution can be elegantly expressed in the distinguishability metric d X , and can be easily incorporated in our algorithm. Finally, we show the applicability of our technique by evaluating on two real-world datasets. We start by building an elastic metric for Paris' wide metropolitan area, in a grid of 562, 500 locations covering an area of 5600 km 2. Privacy mass is computed from semantic information extracted from the OpenStreetMap database, and the whole computation is performed in under a day with modest computational capability, demonstrating the scalability of the proposed algorithm.
We then compare the elastic mechanism to the Planar Laplace mechanism satisfying geo-indistinguishability, on two large areas in the center of Paris as well as a nearby suburb. The evaluation is performed using the datasets of Gowalla and Brightkite  [10] , two popular location-based social networks, and the widely used Bayesian privacy and utility metrics of Shokri et al. [28]. The results show that the dynamic behavior of the elastic mechanism, in contrast to Planar Laplace, provides adequate privacy in both high and low-density areas.
Contributions -We propose the use of elastic metrics to solve the flexibility problem of geo-indistinguishability. We formalize a requirement of such metrics in terms of privacy mass, capturing properties such as space, population, points of interest, etc. -We propose an efficient and scalable graph-based algorithm to compute a metric d X satisfying this requirement. -We show that the technique of geo-fences can be elegantly expressed in the metric and incorporated in the algorithm. -We perform an extensive evaluation of our technique in a large metropolitan area using two real-world datasets, showing the advantages of the elastic metric compared to standard geo-indistinguishability.
The quantity d euc (x, x ) can be viewed as the distinguishability level between the secrets x and x. The use of the Euclidean metric d euc is natural for location privacy: the closer (geographically) two points are, the less distinguishable we would like them to be. Note, however, that any other (pseudo-)metric could be used instead of d euc , such as the Manhattan metric or driving distance, depending on the application. The definition that we obtain by using an arbitrary distinguishability metric d X , i. e. requiring that d P (K(x), K(x )) ≤ d X (x, x ), is referred to as d X -privacy, and is studied on its own right in  [7]. In Section 3 it is argued that a properly constructed d X can provide location privacy while taking into account the semantic properties of each location. It should be noted that standard differential privacy simply corresponds to d h (x, x )-privacy, where d h is the Hamming distance between databases x, x , i.
e. the number of individuals in which they differ. If x, x are adjacent, i. e. they differ in a single individual, then d h (x, x ) = 1 and the distinguishability level between such databases is exactly. Two characterization results are also given in  [2, 7] , providing intuitive interpretations of geo-indistinguishability and d X -privacy. It should be emphasized that geo-indistinguishability aims at protecting the user's location, not his identity (protecting the link between a person identity and its location is the goal of several techniques in the location privacy literature, see Section 6. 0. 0. 6). In our setting a user might even be authenticated with the LBS, for instance to obtain personalized search results, while wishing to protect his location.
Distinguishability level and A point worth emphasizing is the role of the distinguishability level, and its relationship to in each definition. The distinguishability level between two secrets x, x is their distance in the complete privacy metric employed, that is d h (x, x ) for differential privacy, d euc (x, x ) for geo-indistinguishability, and d X (x, x ) for d X -privacy. Secrets that are assigned a "small" distinguishability level will remain indistinguishable, providing privacy, while secrets with a large distance are allowed to be distinguished in order to learn something from the system. Typical values that are considered "small" range from 0. 01 to ln 4; we denote a small distinguishability level by l * , and in our evaluation we use l * = ln 2. In the case of differential privacy, is exactly the distinguishability level between adjacent databases (since d h (x, x ) = 1 for such databases), hence we directly use our "small" level l * for.
We also denote by m(A) = x∈A m(x) the total mass of a set A. The function m(•) should be defined in a way such that a set of points containing a unit of mass provides sufficient cover for the user. Hence, the metric we construct needs to satisfy that m(cover(x)) ≥ 1 ∀x ∈ X Following the idea that privacy comes by hiding within either a "large" or "rich" area, we define m(x) as m(x) = a + q(x)b (1) where a is a quantity assigned to each location simply for "occupying space", q(x) is the "quality" of x and b is a normalization factor. The quality q(x) can be measured in many ways; we measure it by querying the OpenStreetMap database for a variety of POIs around x, as explained in Section 3. 3. Assuming q(x) to be given, we can compute a and b as follows: we start with the intuition that even in empty space, a user feels private if he is indistinguishable within some large radius r large , for instance 3000 m (r large can be provided by the user himself).
Extracting location quality Our definition of privacy mass depends on the semantic quality q(x) of a point x. To compute the quality in a meaningful way, we used the OpenStreetMap database 2  to perform geolocalized queries. The open license ODbL of the database allows to download regional extracts that can then be loaded in a GIS database (Postgresql+PostGIS in our case) and queried for a variety of geo-located features. The data in many urban areas is extremely fine grained, to the level of buildings and trees. Furthermore there is a great variety of mapped objects produced by more that 2 millions users. In order to extract the quality of a cell q(x), we perform several queries reflecting different privacy properties and we combine them in one aggregate number using different weights. In our experiments we query for a variety of Points Of Interest in the tag class amenity, such as restaurants and shops, and for the number of buildings in a cell.
The buildings are an indication of the population density, in fact despite the database provides a population tag, it is for large census areas and with a scarce global coverage, while the building tag can be found everywhere and with fine resolution. Considering the simple nature of the queries performed we believe the resulting grid captures very well the concept of hiding in the crowd that we wanted to achieve (a sample can be viewed in Figure  2a ). We leave more complex query schemes as future work as the main focus of this paper is on the metric costruction, described in detail in Section 3. 4. Among possible improvements three directions seem promising. First, one strength of d X -privacy is that it is independent of prior knowledge that an attacker might have about the user, making the definition suitable for a variety of users.
Practical considerations We believe that the techniques presented are practical enough to deploy a location privacy mechanism in a real setting. The resources required both in terms of hardware and time are very limited, consider that the mechanism evaluated in the next section was built in a day on a medium Amazon E2C instance. As already mentioned querying the database is easily parallelizable and the same grid can be reused to build several metrics. We could imagine having a choice of pre-computed grids, for different flavors of location quality and times (as explained in Sec 3. 3), on top of which the user could tune the requirement. The computation of the metric is the most demanding part of the process but proved reasonably fast for an area that can easily contain all the movements of the user and many optimization are still possible in the algorithm.
We imagine these two computationally intensive activities to be performed by a remote server, with seldom updates from the OpenStreetMap database. The user's phone needs to take care only of downloading an extract of the metric to use in the Exponential mechanism. For every request, the mechanism computes the exponential distribution of sanitized locations and draws from it, which amounts to a trivial computation, both in memory and time. In principle we could also avoid contacting a third party by saving the entire metric locally on the phone, as a reference our metric for Ilê de France is only 58MB. From a user's perspective, the amount of configuration required to run the mechanism varies according to her needs. It can go from no configuration, in the case she downloads a precomputed map, to scaling the privacy mass requirement, to full customization in the case the users wants to tailor the queries to her specific needs.
Evaluation In this section we perform an extensive evaluation of our technique in Paris' wide metropolitan area, using two realworld datasets. We start with a description of the metricconstruction procedure, and we discuss the features of the resulting metric as well as the obfuscation mechanism obtained from it. Then, we compare the elastic mechanism to the Planar Laplace mechanism satisfying geo-indistinguishability, using data from the Gowalla and Brightkite social networks. The comparison is done using the privacy and utility metrics of  [28]. It should be emphasized that the metric construction was completely independent from the two datasets, which were used only for the evaluation. All the code used to run the evaluation is publicly available [1].
The Gowalla and Brightkite datasets Gowalla was a location-based social network launched in 2007 and closed in 2012, after being acquired by Facebook. Users were able to "check-in" at locations in their vicinity, and their friends in the network could see their check-ins. The Gowalla dataset  [10]   These datasets are particularly appealing for our evaluation since a check-in denotes a location of particular interest to the user, and in which the user decided to interact with an actual LBS. This is in sharp contrast to datasets containing simply mobility traces, which just contain user movements without any information about the actual use of an LBS.
Notice how the loss function is not applied directly to the reported location z, but to the remapped location h(z). A rational adversary will use the strategy h * minimizing his error, hence  [28]  proposes to use ADVERROR(K, π, h * , d A ) as a privacy metric, where h * = arg min h ADVERROR(K, π, h, d A ) Note that h * can be computed efficiently using the techniques described in  [29]. In our evaluation the secrets are POIs in each dataset. We use two commonly used loss functions modelling different types of adversaries: the first is the binary loss function d bin , modelling an adversary interested in the semantics of the user's POI, hence trying to guess exactly in which one he is located. d bin (x, z) = 0 x = z 1 x = z Then ADVERROR(K, π, h, d bin ) expresses the adversary's probability of error in guessing the user's POI.
Second, we use the Euclidean loss function d euc , modelling an adversary who is interested in guessing a POI close to the user's, even if that POI is unrelated to his activity. In this case, ADVERROR(K, π, h, d euc ) gives the adversary's expected error in meters in guessing the user's POI. We should emphasize an important difference between the two adversaries: d bin tries to extract semantic information from the actual POI, and is less effective in dense areas where the number of POIs is high. On the other hand, d euc is less sensitive to the number of POIs: if many POIs are close to each other, guessing any of them is equally good. This difference is clearly visible in the evaluation results. We use each dataset to obtain a prior knowledge π * of an "average" user of each social network, by considering all check-ins within the areas of interest.
Note that the datasets do not have enough check-ins per-user to construct individual user profiles. Indeed, most users have checked-in in each location at most once, hence any profile built by n -1 checkins would be completely inadequate for inferring the remaining one. As a consequence, we assume that the adversary will compute his best strategy h * by using the global profile π *. Finally, we use ADVERROR(K, π u , h * , d A ) d A ∈ {d bin , d euc } as our privacy metric, where π u is the user's individual prior (computed only from the user's check-ins), and h * = arg min h ADVERROR(K, π * , h, d A ) is the adversary's strategy computed from the global profile. Hence, the individual priors π u are only used for averaging and not for constructing the strategy.
Results We carry out our evaluation in two different areas of the Paris metropolitan area, with very different privacy profiles. The first area is the city of Paris intra-muros, very private on average, while the second is the adjacent suburb of Nanterre, where already the concentration of privacy mass is much lower. To obtain a fair comparison, the Planar Laplace PL mechanism is configured to have the same utility as the EM mechanism in Paris' center. We computed the utility of EM using the global profile π * from both datasets, using four distance functions, namely d euc and d r with r = 1200, 1500 and 1800 meters. In each case, we computed the parameter of PL that gives the same utility; we found that in all 8 cases the we obtained was almost the same, ranging from 0. 001319 to 0. 001379.
On the other hand, in the suburb there is a sharp degradation for PL. The reason is that the number of POIs in both datasets is much smaller in Nanterre than in Paris, the distance between them is greater, and the resulting priors are more "informed". Hence it is considerably easier for the adversary to distinguish such POIs, leading to a probability of error as low as 0. 62. On the other hand, EM maintains a higher ADVERROR in the suburb, by introducing a higher amount of noise. To match EM's privacy guarantees in the suburb, PL should be configured with a higher amount of noise. However, since Planar Laplace treats space in the same way everywhere, this would lead to a high degradation of utility in Paris' center, which is unfortunate since (i) the extra noise is unnecessary to provide good privacy in the center, and (ii) the extra noise could render the mechanism useless in a dense urban environment where accurate information is crucial.
But the Euclidean adversary is not interested in semantic information, so he scores better with less noise. In the suburb, on the contrary, the picture is reversed. Due to the fact that priors in Nanterre are more "informed" making remapping easier, the adversarial error for PL decreases compared to the city. On the other hand, EM adapts its noise to the less dense environment, leading to much higher adversarial error. Note that the Nanterre area is still quite close to the city center and highly populated itself. The fact that we can already see a difference between the two mechanisms so close to the center is remarkable; clearly, the difference will be much more striking as we move away from the city center (unfortunately, the datasets do not contain enough data in these areas for a meaningful quantitative evaluation).
Finally, we should emphasize that the mechanism's construction and evaluation were completely independent: no information about Gowalla's or Brightkite's list of check-ins was used to construct the metric. The only information used to compute d X was the semantic information extracted from OpenStreetMap. 6 Related Work and Conclusion 6. 0. 0. 6 Related work Concerning location privacy, there are excellent works and surveys  [20, 26, 31]  that present the threats, methods, and guarantees. A large body of works developed from k-anonymity, originally a notion of privacy for databases  [4, 12, 19, 22, 24, 25, 32]. Many of the shortcomings of k-anonymity, outlined in  [30] , are addressed in the current main trend based on the expectation of distance error  [11, 18, 28, 29]. Both are dependents on the adversary's side information, contrary to our approach, as are some other works  [9]  and  [3].
Conclusion In this paper, we have developed a novel elastic privacy metric that allows to adapt the privacy requirement, as hence the amount of applied noise, to the properties of each location. We have formalized a requirement for such metrics based on the concept of privacy mass, and using semantic information extracted from OpenStreetMap. We have developed a graphbased algorithm to efficiently construct a metric satisfying this requirement for large geographical areas. We have discussed how the geo fencing technique can be elegantly expressed in the metric and incorporated in its construction. Finally, we have performed an extensive evaluation of our technique in Paris' wide metropolitan area, using two real-world datasets from the Gowalla and Brightkite social networks. The results show that the adaptive behavior of the elastic mechanism can offer better privacy in low-density areas by adjusting the amount of applied noise.
[10]ains 6, 442, 890 public check-ins from 196, 591 users in the period from February 2009 to October 2010. Of those, 9, 635 check-ins were made in Paris' center area and 429 in the Nanterre area (displayed in Fig 1). Brightkite was another location-based social network created in 2007 and discontinued in 2012. Similarly to Gowalla users could check-in in nearby locations and query who is nearby and who has been in that location before. The Brightkite dataset[10]contains 4, 491, 143 check-ins from 58, 228 users. Of those, 4, 014 check-ins were made in Paris' center and 386 in Nanterre.
The details about the socio-demographic characteristics (collected in a questionnaire at the end of the experiment) of the three subject pools are in the Appendix. Students differ in many dimensions compared to both permanent and temporary workers. Most notably, students are younger, less religious, less likely to be married, with a higher education and characterized by a smaller proportion of women. There are also a few differences between permanent and temporary workers. In particular, permanent workers are older, more likely to be married, more likely to be Christian, and with more years of work experience than temporary workers. In all the other dimensions, we do not detect any statistically significant difference between temporary and permanent workers. Because of these differences between our subject pools, we will also control in the data analysis for the socio-demographic characteristics of subjects 4 SA8000 is a global social accountability standard for decent working conditions.
Experimental Design The experiment is composed of two stages: A classification stage and a main stage. In the classification stage, subjects are required to perform three tasks which are widely employed in the experimental literature to study other-regarding preferences. The subjects first play a one-shot Dictator Game, then they play a Public Good Game to assess their conditional willingness to contribute to a public good (Strategy Method) and finally they play a Decomposed Game. In the main stage, subjects play a repeated anonymous linear Public Good Game (12 repetitions). The experiment was incentivized and fully computerized with the software z-Tree  [29]. Anonymity was guaranteed both during the game and the payment procedures. In the one-shot Dictator Game, each subject is randomly matched with another individual, and has to decide how to divide an endowment of 300 experimental tokens between herself and her matched subject.
Acknowledgments We thank  Formula Servizi  and  Obiettivo Lavoro  for their precious assistance in recruiting the experimental subjects. We thank  Maria Bigoni ,  Stefania Bortolotti ,  Marco Casari ,  Caterina Giannetti ,  Vera Negri Zamagni , the participants at the 2011 BEELab conference in Florence (Italy), two anonymous referees, and the guest editor  Ananish Chaudhuri  for comments and suggestions. Financial support from the  PRIN 2007/B8SC7A_002 , the  University of Bologna Strategic Project "Ethical Values and Competitiveness of Italian Cooperative Companies" ,  CFICEI (Centro di Formazione e Iniziativa sulla Cooperazione e l'Etica d'Impresa)  and  AICCON  is gratefully acknowledged. The usual disclaimer applies.
Among the 9% of university educated women of these cohorts, 50% live in Istanbul and Aegean, while only 2% of university educated women live in Southeast Anatolia. Fertility is high in the Southeast, but at the same time only a minority of Turkish women of all education groups live in this region (8%). Our analysis so far suggests that education, and especially having not completed primary education, is a good proxy for region and ethnicity. We conclude thus that for our micro econometric analysis, taking into account the educational level of women captures quite well their regional background. We will not be able to disentangle effects between education, region and ethnicity, but including education in our regression models will reduce a potential omitted variable bias caused by the fact that we cannot control directly for region (not available in the longitudinal database) and ethnicity (not available in SILC, neither in the cross sectional nor in the longitudinal database).
45% of women are self-employed in Turkey (34% of men) (EU average 13% for women, 19% for men). Women working as formal employees represent thus only 20% of active women in Turkey, while the number is 60% for their male counterparts (EU average around 80% for both men and women, WB WDI, year 2012). The following section includes a discussion of women's activity status, differentiated by child rank, measured by SILC data (tables A to C in the appendix). Uraz et al. (2010)  show that while there is little or no significant difference in female labor market participation between regions (women work in rural areas as well as in urban areas), the impact of the number of children present in the household on women's labor market participation is more negative in urban areas, in particular for low-skilled women.