dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. HOLISTIC PRIVACY FRAMEWORK USING EMCP FOR CLOUD-BASED HEALTHCARE SERVICES The proposed solution is deployed over three primitive usage layers. The first is the data collection layer: consists of various IoHT devices and a mobile application that capture vital sign measurements from the user's body/home during various physical activities. Additionally, the mobile application collects other externally available data related to these performed activities (e. g. location and time). The collected data from this layer is used for recommendation purposes. The second is the intermediate layer: consists of a set of fog nodes where each node hosts a holistic privacy middleware, which will execute the second stage of the two-stage concealment process within a distributed data collection protocol that utilizes the hierarchical nature of IoHT devices.
According to our approach, privacy assurance will be moved from IoHT devices to the personal gateways (called fog nodes). The end-users will need to configure the desired privacy preferences only once (what purposes their health data will be released for, what data from their health profiles gets collected at each concealment level, etc. ), and the fog nodes will have the burden to apply them automatically to any health data released for the cloud based healthcare services, regardless of the physical IoHT device in use. This approach has various advantages: first, establishing a user-centered privacy by transforming the execution of intensive privacy preserving processes from an IoHT device to the fog node; secondly, obtaining a personalized privacy assurance independent from the IoHT devices while reducing their power consumption, which occurs as a consequence of unburdening the IoHT devices from performing these complex processes; thirdly, simplifies adding management and re-configuration of privacy preferences for any released health data and all the IoHT devices; and finally, empowering resource-constrained IoHT devices with the same level of privacy assurance of more secure systems.
Utilizing topological formation within our holistic privacy framework attains privacy for users with relatively low accuracy loss. It also prevents the cloud based healthcare service from creating a centralized database with a raw health data for each user. Additionally, it permits a decentralized execution of a two-stage concealment process on the users' health data. The topological execution in the proposed middleware satisfies the requirements of high scalability and reduces the risk of privacy breaches. The formation of this VPN coalition is done through a specific virtual topology in order to create an aggregated profile (group profile). This topology might be simple like a ring topology or complex like a hierarchical topology (see Figure  2 ). This ordering enables IoHT devices to attain privacy by collaboration between them.
This is not a straightforward task since the two-stage concealment process should make sure that the concealed data is still useful for the recommendation phase, which usually requires that changes on the users' health data be as limited as possible. However, users' health profiles are complicated and are an interrelated structure. Making small changes in it could cause an unexpected influence on the overall recommendation process. The proposed techniques combine approaches from the clustering analysis that consider knowledge representation in the domain of data privacy in order to preserve the aggregates in the dataset to maximize the usability of this data, with a view to accurately perform the desired recommendation process. The validity of the framework is demonstrated by the implementation and evaluation of the proposed solution within a set of important innovative applications.
Finally, secure transmission channels protect all the traffic between the IoHT devices and the fog node, such that the user's released data cannot be eavesdropped or altered by an external attacker before the fog node implements global concealing process Figure  3  illustrates the proposed enhanced middleware for collaborative privacy (EMCP) components running inside the user's fog node. EMCP consists of different cooperative agents. A learning agent explicitly captures vital sign measurements from the user's body/home during various physical activities to build three databases, the first one is the vital sign measurements database, and the second one is the metadata database that contains the feature vector for data related to the measurement of the vital. Finally, the last one is the realized activities database.
The global concealment agent is only exists in the fog node. It executes the global concealment process on the aggregated health profile. The two-stage concealment process acts as wrappers to conceal health data before they are shared with any external cloud based healthcare service. Since the database is dynamic in nature, the local concealment agent periodically conceals the updated vital measurements, and then the synchronize agent forwards them to the cloud based healthcare service (CHS) or the fog node upon owner permission. Thus, recommendations can be made on the most recent health data. The synchronize agent is also responsible for calculating and storing parameterized paths in the anonymous network that attains high throughput, which in turn can be used in submitting the group profile anonymously.
These parameterized paths are stored in a database called ''nodes store''. The policy agent is the entity in EMCP that has the ability to encode privacy preferences and privacy policies as XML statements. It also has the responsibility to encode data collection and data usage practices as P3P policies via XML statements which are answering questions concerning the purpose of collection, the recipients of these health profiles, and the retention policy. In order to do so, the policy agent needs to acquire the user's privacy preferences and express them using APPEL as a set of preferences rules which are then decoded into a set of elements that are stored in a database called ''privacy preferences'' in the form of tables called ''privacy meta-data''. These rules contain both privacy policy and an action to be taken for such privacy policy, in this way, it will enable the preference checker to make self-acting decisions on data points that are encountered during the data collection process regarding different P3P policies (e.
g. : privacy preferences could include: certain  categories of data points should be excluded from health data before submission, refresh rate of vital sign parameters, usage of data points that have been captured during sensitive activities/locations, generalize certain data points in user's health profile according to defined taxonomy, using synonyms for certain terms or names in user's health profile, suppressing certain data points from the extracted data and insert dummy data points that have the same feature vector like the suppressed ones as described in  [20] , limiting the potentially output patterns from extracted health data etc. in order to prevent the disclosure of sensitive data points in user's health profile). Query Rewriter rewrites the received request constrained by the privacy preferences for its host.
Additionally, it selects a set of its outdated measurements and recommendations' results to be shared later with external fog nodes, in order to compute the trust level. The local concealment agent is employed to execute the local concealment process on any released data. Finally, the manager agent dispatches this data to the individual fog nodes that have decided to participate in the computation of the trust level. 3. Upon receiving replies from different fog nodes, the manager agent negotiates with the security authority center (SAC) to select the fog nodes with the highest reputation to act as members in the trust computation. The reputation score for each fog node is calculated as a resultant of the trust ratings assigned to it. The reputation score of each fog node serves as a weight for any evaluation that this fog node assigns to each contribution in the trust computation.
SAC is a trusted third party responsible for making an assessment on those fog nodes according to the fog nodes' reports. Moreover, SAC records the calculated reputation scores for various fog nodes and trust levels for different cloud based healthcare service providers. 4. The manager agent negotiates with the cloud based healthcare service to express its privacy policies for the data collection and usage process via P3P policies. 5. When the manager agent receives the P3P policy from the cloud based healthcare service, it forwards the received P3P policy and the patient's request to the preference checker and the query rewriter respectively. The preference checker ensures that the extracted health measurements do not violate the privacy of its host which was previously declared by the use of APPEL preferences.
The privacy obtained through the proposed holistic privacy approach is as follows: • Collection Method: The proposed solution attains an explicit data collection mode. End-users are aware that a data collection within a recommendation process is happening and they can make a wise decision about whether or not to provide their health data in this recommendation process. Privacy policies such as P3P are utilized to explain to the users how their health data is going to be used. End-users utilize privacy preferences in order to control what health data from their profiles gets collected at each concealment level. However, formalizing such privacy preferences is not an easy task. End-users need to realize various privacy issues. End-users also need to deduce future recommendation requests that might raise privacy concerns for their collected health data.
The end-user can employ an anonymous network while sending this locally concealed health data to either the fog node or the cloud based healthcare service. • Duration: The proposed solution attains a sessionbased collection that allows for a simpler service that does not need the storage and retrieval of users' health profiles. The data related to the recommendation process is collected from users' health profiles in a concealed form. This concealed data is only feasible for the recommendation purposes. This reduces privacy concerns since minimal data to be collected and also ensures compliance with privacy laws. The concealed data is stored at the cloud based service in order to enhance the recommendation model and future requests. Moreover, this health data by default is protected by the retention policies of data protection laws.
• Specifying their privacy preferences: End-users can express their privacy preferences using APPEL as a set of rules which are then decoded into a set of elements that are stored in a privacy preferences database. These rules will enable EMCP to make self-acting decisions on data elements that are encountered during the data collection process regarding different P3P policies. • Reducing the impact of privacy breaches: In case the occurrence of privacy invasion happens at the cloud based healthcare service, the leaked users' health data will be worthless with a diminished informative value, because it is already concealed with a two-stage concealment process and cannot be linked directly to a specific user. The leaked users' health data is also concealed in a way to be only useful for recommendations purposes and it would be difficult to perform different kinds of analytical processes on such data.
A. CASE STUDY: HEALTHCARE RECOMMENDER SERVICE We consider the scenario where a healthcare recommender service (CHS) is implemented as an external cloud based service and end-users give information about their health data to that service in order to receive personalized health insights. The user's health data is stored in his/her profile in the form of measurements for different vital signs; such measurements are implicitly recorded by the various IoHT devices. The normal ranges for these measurements depending on gender, age, weight, and overall health status of the patient. The fog node at the end-user side will explicitly extract the recorded measurements from the various IoHT devices to reconstruct a detailed health profile, which will be used by a cloudbased healthcare recommender service.
This is a realistic assumption because the service provider needs to accomplish some business goals and increase its revenues. In this scenario, we will use the fog node storage to store the end-user health profile. However, the healthcare recommender service maintains a centralized database for storing the group profiles that is used in building and training the recommendations' models. Additionally, we alleviate the user's identity problems stated above by using anonymous pseudonyms identities for end-users. The recommendation process based on the two-stage concealment process in our framework can be summarized as follows: 1. In the setup phase, EMCP is deployed in the user's fog node, which in turn will be responsible for the concealment of any data that is collected by the healthcare recommender service.
The local profile is stored in three databases, the first one is the vital sign measurements database that contains (DeviceID, time_date, measurement_type, measurement_value) and the second one is the metadata database that contains the feature vector for data related to the measurement of the vital sign (id, feature1, feature2, feature3). The feature vector can include: normal range, average readings, atmospheric pressure, moisture, humidity, temperature, location and so on. Finally, the last one is the realized activities database that contains (type of activity, path length, time interval, average speed, location). 3. Based on user's appeal, the manager agent within the EMCP broadcasts a message to various IoHT devices placed on the patient's surrounding environment to request recommendations for a personalized life-style that could potentially improve the health condition of its owner.
The IoHT device with a sufficient amount of health data performs a local concealment process to conceal its log-file. The log-file is a lightweight embedded database which is hosted on each IoHT device that is responsible for storing vital sign readings, which were locally collected by this device. Finally, The IoHT devices submit their locally concealed health data to the manager agent using a predefined secure WI-FI or Bluetooth connection. 4. After the manager agent receives all the devices' data, it constructs an aggregated profile (group profile), then, executes a global concealment process to conceal this group profile. Then after, it can interact with the cloud based healthcare recommender service by acting as an intermediary node that has the group profile as its own health profile.
One practical issue that must be dealt with when using the Paillier cryptosystem is the fact that it cannot naturally encrypt floating-point numbers. Floating-point numbers must be converted to a fixed-point representation. This is done by multiplying them by a large constant and then truncating the result to an integer. 2. The Aglets library was used to build different agents within the proposed EMCP middleware, which are running inside the user's fog nodes. 3. P3P policies and APPEL preferences rules standards were used to encode data collection, usage practices, and their actions. 4. MySQL database was used as data storage for storing users' health profiles, policies, and privacy preferences that were acquired by the EMCP middleware. 5. Tor network was used to attain anonymity when sending data between different parties within the system, either between the IoHT devices and fog nodes or between the fog nodes and the cloud based healthcare recommender service.
6. The experiments were conducted using a dataset pulled from the SportyPal network 10  that was linked to another dataset containing health parameters (blood pressure, heart rate, electrocardiogram, blood glucose, and respiratory rate) of 6000 students in one of the universities in North America in the period of 2010 to 2012. In order to set-up the proposed holistic privacy framework, the end-users have to install the EMCP middleware on their personal gateways (Setup box or mobile phone). Then after, they relocate their stored health profiles into vital sign measurements, meta-data and realized activities databases within the learning agent. Finally, they formalize their privacy preferences and actions for the various policies. The healthcare recommender services are only required to offer P3P-compliant service by encoding their data collection and data usage practices in the form of P3P policies.
Fog nodes and Healthcare recommender services use a platform for privacy preferences (P3P) policies for specifying their data usage practices. While end-users describe their privacy constraints for the data extracted from their health profiles in a dynamically updateable fashion using P3P policies exchange language (APPEL). The proposed framework allows a fine-grained enforcement of privacy policies by allowing end-users to ensure the extracted preferences for specific recommendation requests do not violate their privacy by automatically checking whether there is an APPEL preference corresponding to the given P3P policy. Fog nodes aggregate the vital measurements obtained from the underlying IoHT devices, encapsulate them in a group profile, and then send it to the healthcare recommender service.
We have tested the performance of the proposed framework on a case study for healthcare recommender service using a real dataset. We evaluated how the overall accuracy of the recommendation varies based on various parameters of the two-stage concealment process. The experimental and analysis results show that privacy increases under the proposed framework without hampering the accuracy of the recommendation. Thus, adding the proposed framework does not severely affect the accuracy of the recommendation based on the off-the-shelf recommendations techniques. Upon the end of this research work, we realized that there would be many challenges in building a holistic privacy framework for cloud based healthcare services. As a result, we focused on a middleware approach in our holistic privacy solution.
A future research agenda will include utilizing game theory to better formulate groups of IoHT devices, sequential vital measurements release and its impact on the privacy of the whole health profile. Furthermore, it is included to strengthen our holistic privacy framework against shilling attacks, extending our scheme to be directed towards multidimensional trust propagation and distributed collaborative filtering techniques in a P2P environment. We also need to perform extensive experiments on other real datasets from the UCI repository and compare our performance with other techniques proposed in the literature. Finally, we need to consider different data partitioning techniques as well as identify potential threats and add some protocols to ensure the privacy of the data against those threats.
His current research interests include database, big data analysis, music retrieval, multimedia systems, machine learning, knowledge management, and computational intelligence. He has authored 100 papers in refereed journals and conference proceedings in these areas. He has received a few awards, including the Who's Who in America, the Who's Who in Science and Engineering, and the Who's Who in the World in 2007 and 2008. He has been involved in over 20 conferences and workshops as various chairs and over 30 conferences/workshops as a Program Committee Member. He has edited a number of international journal special issues as a Guest Editor, such as multimedia systems, information fusion, engineering applications of artificial intelligence, new review of hypermedia and multimedia, multimedia tools and applications, personal and ubiquitous computing, telecommunication systems, and ad hoc & sensor wireless networks.
This work was supported in part by the  Dirección General de Investigación, Innovación y Postgrado of Federico Santa María Technical University-Chile , in the Project  Private and Secure Mobile Cloud Framework for Medical IoT Devices  ( UTFSM-DGIP 116. 23. 3 ), in part by the  Basic Science Research Program  through the  National Research Foundation of Korea Funded  by the  Ministry of Education  under Grant  2013R1A1A2061978.
For each category, frequency is indicated. (B) Distribution of total and differentially used CAGE promoters overlapping with epigenetically defined promoters and enhancers. (C) Annotation of total and differentially used CAGE promoters. The graphs show the proportions of total and differentially used CAGE promoters associated to coding RNA and ncRNA (miRNA, rRNA, snoRNA and snRNAand lincRNA). We defined > 13,000 CAGE promoters of similar average length (160 ± 101 bp) in each cell type as clusters of nearby TSSs (13,852 in HSPC, 13,609 in MPP and 14,041 in EPP), > 96% of which overlapping with epigenetically defined promoters (Fig. 2B ). We assigned CAGE promoters to the closest transcript using publicly available datasets. The majority (~80%) of CAGE promoters were annotated to known genes and particularly to protein-coding transcripts (Fig.
Retroviral scanning defines a sub-population of mostly cell-specific regulatory elements. We mapped and analyzed the distribution of > 27,000 MLV integration sites in each cell population. About 22% of MLV integrations occurred in TSS-proximal regions and the remaining ones were equally distributed in intergenic and intragenic regions (Supplementary Fig. 10A ). Statistical comparison with a random dataset identified a total of 3,498, 2,989 and 4,103 integration clusters in HSPC, EPP and MPP, respectively, with a comparable median span of 5. 9, 6. 0 and 4. 8 kb (see Supplementary Methods). Virtually all clusters overlapped with epigenetically defined regulatory regions, two-thirds in enhancers and one-third in promoters (Fig. 6A ). However, integration clusters targeted only a small fraction of ChIP-defined regulatory regions, i.
6E  and Supplementary Fig. 11A ). In addition, genes flanking cell-specific MLV-targeted enhancers are functionally linked in cell-specific pathways (Supplementary Fig. 11B ). The same regions were moderately enriched in hematopoietic TF motifs (Fig. 6F ). Comparison with TF ChIP-seq datasets showed that a considerable fraction of MLV cell-specific clusters was targeted by HSPC-, EPP-and MPP-related TFs (Supplementary Fig. 12 ). To determine whether the putative enhancers identified by combining ChIP-seq and retroviral scanning have transcriptional activity in a functional assay, we tested 8 MLV-targeted erythroid and myeloid enhancers in a reporter assay in EPP and MPP respectively. As expected, erythroid-specific MLV-targeted regions had higher activity in EPP than in MPP and vice versa (Fig.
7A ), confirming that MLV identifies cell-specific enhancers, (C) Expression levels of CAGE promoters surrounding HSPC-, EPP-and MPP-specific enhancers (± 5 kb interval) in HSPC, EPP, MPP, embryonic stem cells (ES) and keratinocytes (KER). As control, expression levels of total HSPC, EPP and MPP CAGE promoters were analyzed. A t-test was used to determine significant differences in the expression values associated to CAGE promoters in the different cell types (*P < 0. 05). The median expression levels of genes close to weak/inactive enhancers was similar among the different cell populations (data not shown). Comparable results were obtained by analyzing the expression levels of CAGE promoters in a 100-Kb window (data not shown).