dataesr/scientific-paragraphs-categorization huggingface dataset:
The tracker then is now becomes a first party and can set a tracking cookie. Finally, the tracker redirects to the initial website 3 , while it also remains able to track the devices across websites. One cutting edge web browser that promises anonymity is called the Tor Browser Bundle 5  (TBB) or the Tor Browser, whose anonymity guarantees have been widely studied  [8, 17]. Up to version 5. 5, TBB developers tackled font-based tracking by introducing two limits: one on the number of font load trials, and one on the number of successful font loads  [20]. These parameters are called browser. display. max_font_count and browser. display. max_font_attempts, and by default they are set to a value of 10. These options can be changed by accessing about:config. Detecting the existence of fonts on a system is relatively easy using Javascript, which can be embedded on the visited website: it can be measured if the rendered text has a different width and height compared to the system default  6.
Then, using the collected dataset of font profiles, the adversary can compute the fingerprint fonts of the user, which can be later queried in order to de-anonymize the user when he visits a site through TOR containing a malicious tracker 4. In case of general fingerprinting, the adversary computes the general fingerprint using the collected dataset of font profiles, and queries these fonts from each TOR visitor 4. ated with the user's identity. We note that this does not mean that the site is under a total control of the adversary. This can also be a site where a malicious tracker (or web bug) is installed. Then, the adversary selects at most 10 fingerprint fonts whose existence need to be checked on the user's system to re-identify him/her. When the user visits this or another malicious website via Tor Browser, this site queries these fingerprint fonts through Javascript.
This is analogous to our general fingerprinting problem, where the maximum number of fingerprint queries is limited to 10. Notice that while these fingerprints may not be sufficiently unique to every user in the whole TBB community, they should be quite unique per site. According to a measurement based on TBB usage data of 2012-2013  [15] , the average number of daily Tor Browser users per country was well below 1%, e. g. , 0. 08% in Europe, meaning that even visitors of well known websites receiving a large daily traffic could be easily identified even if they would use TBB. In order to identify the fingerprint fonts, the adversary needs to have access to a subset of all profiles in the TBB population, where a profile contains the list of installed and non-installed fonts of a user.
Mass de-anonymization in location datasets We can also use fingerprinting techniques in a quite different context than online tracking, that is, to deanonymize a location database (e. g. , a Call Detail Record dataset). Suppose that the adversary has access to a weakly anonymized location dataset containing the set of visited locations per individual in a city (time information is omitted for the sake of simplicity). For example, this dataset contains pseudonymized records (i. e. , only direct personal identifiers are removed), but location information are kept intact. The goal of the adversary is to re-identify as many users as possible in this dataset, i. e. , to perform a mass de-anonymization attack without knowing any locations of the victims a priori  7  , but only their personal photos (e.
g. , by crawling a social media of the city). The plan of the adversary is to mount at most s cameras at different spots of the city, and reidentify all individuals observed by any of these cameras by using some face recognition mechanism and the photos that she has. Knowing the fact that an individual does (not) visit these camera spots (i. e. , the visiting pattern of individuals with respect to only these spots), the adversary locates the record which has the observed visiting pattern in the anonymized dataset, and learn all other unknown visited places of the individual. 8 n order to maximize the number of de-anonymized users in the dataset, the adversary wants to identify at most s camera spots which can distinguish all users in the anonymized dataset as much as possible. In particular, the values of any record at these spots should be unique in the dataset, or at least the number of records sharing the same values at these spots should be minimized.
This goal is reminiscent of our general fingerprinting problem. Hence, the adversary runs our greedy heuristic in Algorithm 2, with input s and the anonymized dataset, in order to identify these fingerprint spots. Although deploying cameras at any spot of a city is unlawful and easily detectable, we believe that the above scenario is not far-fetched at all. Authorities, who have the right to install or access such surveillance cameras, may be interested in the above "privacy-friendly" approach of learning the trajectories of suspects. Indeed, authorities do not need to ask telecom companies to reveal the exact identity of any of their customers, but only their anonymized CDRs.
Dataset The dataset comes from the Carat research project  [25]. The dataset includes data from 54, 893 Carat Android users between 11/03/2013 and 15/10/2013  [26]. During this period, the Carat app 9  was collecting the list of running apps (and not the list of all installed apps) on users' devices when the battery level changes. As collecting the list of running apps multiple times over more than 7 months is likely to sum up to the set of all installed apps of a user, we consider a record as the set of installed applications in this paper, even if a record might not be the complete set of installed apps all the time. We removed system apps from all records because they are common to all users. Without system apps, our analyzed dataset contains 92, 210 different applications whereas the total number of apps available on the GooglePlay were around 1 million during this time  10.
Ethical considerations This dataset contains the list of installed smartphone applications per user. The data were collected with the users' consent, and they were explicitly informed that their data could be used and shared for various research projects. In fact, the Carat privacy policy (available at http://carat. cs. helsinki. fi) clearly specifies that "Carat is a research project, so we reserve the right to publish our results online and in academic publications. We also reserve the right to release the data sets into the public domain. " Also, the dataset was shared with us by the Carat team in a pseudo-anonymised form. In particular, identifiers were removed, and each application name was replaced with its SHA1 hash. It contained 54, 893 records  [26] , i. e. one record per user. Each record is composed of the list of applications installed and run by the user.
Dataset This dataset contains the list of installed fonts, the screen size and available screen size per user. The data collection was done on fingerprint. pet-portal. eu where users could test and compare their OS fingerprint in different browsers. Data was collected with the purpose of analyzing OS fingerprint uniqueness. In particular, users were advised to run the test in different browsers which collected the list of installed fonts, among others, and compare the calculated fingerprint if they match The website have collected more than 107, 102 font profiles between 05/04/2012 and 22/09/2015, which do not necessarily belong to distinct users due to the purpose of the project. In our experiments, we only used a sub-  set of users and attributes that were collected, and the data was cleaned as follows.
In order to remove possible duplicates (a user could visit the fingerprinting site multiple times), several records were removed by comparing hashed IPs and stored usernames. If username was not given by a person who conducted the test, the site stored the font profile of the user in an evercookie  11. Given a list of profiles having the same IP address where creation dates were closer than 10, only the first entry was preserved. All record duplicates having the same username were removed regardless of date differences. The resulted database had 43, 656 records, which contained the list of detected fonts, and the screen and available screen sizes 12 of each user. As this is the first paper using the given dataset, we provide descriptive metrics of the data in order to help understanding our results.
Ethical considerations The data were collected with users' consent on a website that raises awareness on tracking using OS (or device) fingerprinting. On the website it is clearly described that all collected information is treated confidentially, and the goal of the data collection is to analyze uniqueness of generated OS fingerprints. Types of data that are collected is also clearly communicated, from which we used screen size, available screen size, and available fonts per user. The data we used was shared with us with the operators of the fingerprint website, and our agreement allowed us to conduct the uniqueness study we present in this paper.
Dataset This dataset contains the list of visited locations per user in a large European city. We use a CDR (Call Detail Record) dataset provided by a cell phone operator in Europe. A cell tower is visited by an individual, if the operator has a recorded event at the tower related to the individual over the observed period (01/09/2007 -17/10/2007). An event can be an incoming/outgoing call or message to/from the individual. The dataset contains the events of 4, 427, 486 users at 1, 303 towers within the administrative region of the city, where the GPS coordinates of all the towers are also available. In our dataset, a record contains only the set of towers that are visited by a user over six weeks, i. e. , it does not contain the time of visits. The average number of individuals per tower over this period was 38, 817 with a standard deviation of 50, 911.
Ethical considerations The dataset was shared with us by the telecom operator in a pseudo-anonymised form. Each record in this dataset represented an event belonging to a single user with a pseudonymized phone number, i. e. , the last 8 digits of the original phone number were changed to a randomly chosen 8 digit number. The record included the GPS location of the cell tower, the type of the event (incoming/outgoing SMS or call) and the timestamp of the event. Records (events) belonging to the same individual contained identical pseudonymized phone number, hence all events of an individual could be linked together. We signed a data sharing agreement with the operator which stipulated that we cannot use the dataset to deanonymize the users in the dataset.
Smartphone users by applications We run Algorithm 1 on the smartphone application dataset, described in Section 6. 1. 1, to generate an individual fingerprint for each user in our dataset with different constraints on the maximum size of the fingerprint. When s = 50 (this threshold comes from the iOS countermeasure detailed in Section 5. 1. 2), on average, only 2. 3 app queries per user provided the fingerprint value. The exact distribution of fingerprint length is shown in Fig. 6. Our greedy algorithm generated fingerprints shorter than 50 apps for all users. Moreover, with the exception of 27 users, the generated fingerprints consist of at most 10 apps. The reason is that our algorithm can terminate before the fingerprint size reaches s (see Line 7 in Algorithm 1). We note that a user who has a fingerprint shorter than s is not necessarily unique with this fingerprint in the dataset.
For example, a user may have fewer than s apps installed on his phone which are also installed by other users in the dataset. For s = 50, we find 4,098 users (7. 47%) in our dataset who cannot be uniquely re-identified and therefore belong to some anonymity sets with size greater than 1. We visualized the sizes of anonymity sets and their population in Fig. 8a. This figure shows that 96. 15% of users are almost unique (the size of their anonymity sets is at most 3), while only 3. 02% of users fall into anonymity sets with size at least 10. (Note: large anonymity sets concerning only a couple of users are possible as these anonymity sets are not disjoint. ) We also measured the size anonymity of sets when s = 2 (instead of 50); results are reported in Fig. 8b. The proportion of almost unique users decreased to 85.
54%, eventually showing that the current privacy protection method on iOS is ineffective against fingerprinting users. In addition, this uniqueness value is larger than the one in  [4] , which was about 75% for 2 apps. The difference, as we have discussed in Section 1, is due to the fact that only existing apps of users are selected to measure their uniqueness in  [4] , meanwhile we optimize the queries for the targeted user's profile and they can also include the queries of non-existing apps. Finally, we note that similar uniqueness is expected in even larger datasets based on the same reasoning as in  [4]. With a growing number of users, the number of available applications should also increase with a lower pace. This means that the number of possible combinations of apps also increases exponentially with each new application added to the dataset.
TBB users by font detection We also run Algorithm 1 on the font dataset, described in Section 6. 1. 2, to measure the effectiveness of fingerprinting using fonts. We believe that, despite that non-Firefox browsers are not excluded from our dataset, our results are generic. While there are minor differences of JavaScript font detection in different browsers, this method is reliable enough to detect the majority of fonts in all browsers. As a result, a record characterizes the OS of a user rather than his browser. We also studied the uniqueness of fingerprints when they are extended with the screen resolution and available screen size. While the Tor Browser makes various efforts to hinder access to this information, it cannot block full access  [20] ; for example, it warns users of related privacy hazards in case the window of a Tor Browser is maximized.
The distribution of fingerprint length is shown in Fig. 7. Results are better for user privacy compared to our app dataset, as only 53. 8% of users have fingerprints no longer than the font load limit of s = 10 imposed by the Tor Browser (see Section 5. 2). Peaks appearing for longer length are likely to be due to pre-set OS configurations that were not altered at all, or only slightly. For example, these can be computers running in institutions or at companies, where configurations are very similar to each other. Barely modified fresh installs could also be behind this phenomena. As font fingerprints are not necessarily unique (due to the same reasons discussed in Section 6. 2. 1), we report the sizes of anonymity sets for each user. Results are shown in Fig. 9a. Detection of 10 fonts allows unique identification of 13, 155 users in our datasets (30.
General fingerprinting We run Algorithm 2 on the three datasets to compute general fingerprints and partition users into anonymity sets, where users have the same fingerprint value in each set. The average size of anonymity sets for small finger-   and location dataset, respectively. In case of the application dataset, the average anonymity set size is 1. 96 at s = 29. In case of the other datasets, the average never falls below 3, and we found 3. 99 at s = 76 for the app dataset, and 3. 96 at s = 73 for the font dataset. While these results may not be promising to the adversary, measuring the proportion of unique users, whose anonymity sets are singletons, provides a different view. Unique users do not share the same fingerprint value with any other users in the dataset. As Fig. 12  shows, the proportion of unique users remains relatively low.
The proportion does not change too much even for almost unique users which have anonymity set with size at most 3. As regards linking attack against TBB users, we find that the available screen size as an auxiliary information (in addition to the detection of 10 fonts) makes this attack a significant threat, as 8, 303 users (19. 02%) are unique in this case. Without available screen size, less than 0. 37% of users are unique with s = 10, but 7. 89% of users are almost unique with s = 25 (Fig. 14 ; with available screen size in Fig. 16 ). We provide further  details in Fig. 13  on the apps dataset, where uniqueness was the highest, that is, 76. 30% of users have a size of anonymity set smaller than 4. This means that even if all 50 apps need to be pre-defined for all users, which is the case for iOS 9 at present, privacy threats still persist.
Conclusion In this paper we addressed two fingerprinting problems with constraints on the maximum size of the fingerprint. Targeted fingerprinting is executed on a specific user, whose profile and identity are known to the adversary, and the goal is to re-identify him/her in an  anonymous context, where only a limited number of his attributes can be queried. General fingerprinting is used to link the activities of multiple users without their reidentification in an anonymous context, where only a limited number of their attributes can be queried. We provided essentially the best fingerprinting algorithms for both problems. We evaluated these fingerprinting methods on reallife examples focusing on web tracking, smartphone user identification and mass de-anonymization of location datasets. We showed that targeted fingerprinting is a significant threat to user privacy, as a significant ratio of users can be re-identified with our technique in case of web tracking and smartphone identification.
This holds even when tighter constraints are applied than the default. Our results with general fingerprinting also support this claim; we found that 19. 02% of web browsers could be uniquely identified by combining the presence of 10 fonts and the available screen size. We also found that by installing 100 surveillance cameras in a city of 4 million people, one can re-identify on average every fourth person in pseudonymized CDR dataset. Moreover, by querying 50 apps on a smartphone, which can be even pre-defined and identical for all users, one can obtain a unique fingerprint value for more than 65% of users in a dataset of 55k users. All these findings show a conceptual weakness of limiting attribute access as a privacy guard.
Dataset size 4, 427, 486 # of all cell towers 1303 Maximum record size 422 Minimum record size 1 Average record size 11. 42 Std. dev of record size 17. 23 Total area of all cells 128. 1 km 2Table 5. 5 Characteristics of our location dataset (up) andVoronoi-tessellation of cell towers (down). Red lines denote the boundaries of districts.
All samples were wet sieved to isolate the <63 lm size fraction, as >63 lm grains tend to be transported by winds primarily by saltation rather than in suspension and thus have minimal transport distances  (Tsoar and Pye, 1987). The <5 lm fraction, taken to represent dust transported long (>1000 km) distances, was then separated by settling at LDEO. 63 and 5 lm were chosen to facilitate comparison with other datasets, as these size separations are commonly used in investigations of dust provenance (e. g. ,  Gaiero et al. , 2004; Revel-Rolland et al. , 2006). For most samples from East Asia, bulk samples were separated into several size fractions by settling in ultrapure water at the State Key Laboratory of Loess and Quaternary Geology in Xi'an. For these samples, the finest grain size fraction is <4 lm.
Samples from 15 ocean sediment cores in which lithogenic fluxes are thought to dominantly derive from eolian dust were analyzed for comparison with dust source area data (Fig. 1 ; Table  EA2 ). For most cores, three or more samples were analyzed to test for reproducibility of 4 He/ 232 Th ratios. We attempted to include only samples from the Holocene and late Pleistocene. In regions in which dated cores were not available, we chose piston cores with Pleistocene basal ages and sampled within the top one meter. For East Asian dust, we chose one loess-paleosol sequence at Zhaojiachuan (ZJC) and two cores (ODP1208, VM32-126) from a region of the North Pacific in which Asian dust has been identified as the dominant source of lithogenic inputs  (Nakai et al. , 1993; Weber et al. , 1996; Jones et al.
, 2000). We also include results from nine North Pacific coretops studied by  Serno et al. (2014)  in which rare earth element patterns and grain size data suggest that lithogenic material is >85% eolian dust. For Australia, two Tasman Sea cores were chosen (RC9-128, RC9-131). Australian dust is thought to be the dominant source of lithogenic sediments in this region  (Hesse, 1994; Kawahata, 2002) , though hemipelagic material from New Zealand is an additional potential contributor. For transport of dust from South America within westerly winds, we chose three south Atlantic cores (VM22-108, RC15-93, RC15-94) in a region where model results indicate that South America is the primary source of dust  (Li et al. , 2008). Kumar (1994)  previously concluded that non-eolian lithogenic inputs were negligible in these cores.
Thorium data 232 Th concentrations were measured by isotope dilution at LDEO  (Anderson and Fleer, 1982; Fleisher and Anderson, 2003). Aliquots of 5-10 mg were taken from larger samples of dried, homogenized sediment were spiked with a 229 Th-236 U solution. Small sample sizes for dust source area samples were required due to limited sample availability, particularly for the fine fraction. A combination of concentrated HClO 4 , HF and HNO 3 were added to dissolve the sample, and the solution was dried down to a viscous bead on a hot plate. A fraction of the samples (18 out of 108) were then taken up in 0. 25 N HNO 3 + 0. 02 -N HF and analyzed after this step. For the majority of the samples, U and Th were removed from solution by Fe oxy-hydroxide co-precipitation. The samples were then centrifuged and the supernatant decanted to remove other solutes.
ACKNOWLEDGEMENTS We thank  P. De Deckker  and  P. Vallelonga  for providing samples and valuable input and  Igor Tolstikhin  for helpful discussions. Martin Fleisher ,  Linda Baker  and  Roseanne Schwartz  provided important assistance and input with sample preparation and analysis. We thank editor  Dominik Weiss ,  Sujoy Mukhopadhyay ,  Franco Marcantonio  and one anonymous reviewer for thoughtful and constructive reviews that improved the manuscript. The  Ocean Drilling Program  provided samples from  ODP Sites 882  and  1208. Partial support for this work was provided by  NSF  award  OCE-1060907  to G. W. and R. F. A. and OREGSU S1315A-D to G. W. , a sub-award to NSF award  PLR 0968391 ).
Subsequently, NUPR1 protein expression in HCC cells was investigated by immunofluorescence analysis after sorafenib treatment. As shown in Figure  2c , NUPR1 was localized in the nuclei of HCC cells, and its expression increased after sorafenib treatment. NUPR1 mRNA expression was similarly induced in HCC cell lines in a dose-and time-dependent manner upon sorafenib treatment (Figures  2d  and e ). Bioinformatics analysis of NUPR1 gene expression in liver cancer. To explore the clinical relevance of NUPR1 in liver cancer development, we analyzed the HCC microarray data sets available on the Oncomine software. 4] [25] [26] [27] [28] [29] [30]  The data summarized in Supplementary Table  S2  show that NUPR1 transcript levels were significantly lower in cirrhosis and liver cell dysplasia than in NL tissue, while higher NUPR1 gene expression levels were detected in HCC samples than in normal and cancer precursor tissues.