Thematische Bibliographien / Private Data Analysis

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Auswahl der wissenschaftlichen Literatur zum Thema „Private Data Analysis“

Autor: Grafiati

Veröffentlicht am 15. März 2025

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Zeitschriftenartikel zum Thema "Private Data Analysis"

Shi, Elaine, T. H. Hubert Chan, Eleanor Rieffel und Dawn Song. „Distributed Private Data Analysis“. ACM Transactions on Algorithms 13, Nr. 4 (21.12.2017): 1–38. http://dx.doi.org/10.1145/3146549.

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Abdul Manap, Nazura, Mohamad Rizal Abd Rahman und Siti Nur Farah Atiqah Salleh. „HEALTH DATA OWNERSHIP IN MALAYSIA PUBLIC AND PRIVATE HEALTHCARE: A LEGAL ANALYSIS OF HEALTH DATA PRIVACY IN THE AGE OF BIG DATA“. International Journal of Law, Government and Communication 7, Nr. 30 (31.12.2022): 33–41. http://dx.doi.org/10.35631/ijlgc.730004.

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Health data ownership in big data is a new legal issue. The problem stands between the public and private healthcare as the main proprietor of health data. In Malaysia, health data ownership is under government hospitals and private healthcare jurisdictions. Who owns the data will be responsible for safeguarding it, including its privacy. Various technical methods are applied to protect health data, such as aggregation and anonymization. The thing is, do these technical methods are still reliable to safeguard privacy in big data? In terms of legal protection, private healthcare is governed under Personal Data Protection Act 2010, while the same Act does not bind the government. With the advancement of big data, public and private healthcare are trying to extract values from health data by processing big data and its analytical outcomes. Considering that health data is sensitive due to its nature which contains personal information of individuals or patients, had raised an issue as to whether the proprietor could provide adequate legal protection of health data. Personal Data Protection Act 2010 is still applicable in giving protection for health data for private healthcare, but what are the laws governing health data privacy in public healthcare? This article aims to answer the questions by analyzing legal sources relevant to health data privacy in big data. We propose a regulatory guideline that follows the GDPR as a legal reference model to harmonize the public and private healthcare ownership of health data better to protect the privacy of individuals in big data.

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Dwork, Cynthia, Frank McSherry, Kobbi Nissim und Adam Smith. „Calibrating Noise to Sensitivity in Private Data Analysis“. Journal of Privacy and Confidentiality 7, Nr. 3 (30.05.2017): 17–51. http://dx.doi.org/10.29012/jpc.v7i3.405.

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We continue a line of research initiated in Dinur and Nissim (2003); Dwork and Nissim (2004); and Blum et al. (2005) on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function $f$ mapping databases to reals, the so-called {\em true answer} is the result of applying $f$ to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which $f = \sum_i g(x_i)$, where $x_i$ denotes the $i$th row of the database and $g$ maps database rows to $[0,1]$. We extend the study to general functions $f$, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the {\em sensitivity} of the function $f$. Roughly speaking, this is the amount that any single argument to $f$ can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean definition of privacy---now known as differential privacy---and measure of its loss. We also provide a set of tools for designing and combining differentially private algorithms, permitting the construction of complex differentially private analytical tools from simple differentially private primitives. Finally, we obtain separation results showing the increased value of interactive statistical release mechanisms over non-interactive ones.

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Proserpio, Davide, Sharon Goldberg und Frank McSherry. „Calibrating data to sensitivity in private data analysis“. Proceedings of the VLDB Endowment 7, Nr. 8 (April 2014): 637–48. http://dx.doi.org/10.14778/2732296.2732300.

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Mandal, Sanjeev Kumar, Amit Sharma, Santosh Kumar Henge, Sumaira Bashir, Madhuresh Shukla und Asim Tara Pathak. „Secure data encryption key scenario for protecting private data security and privacy“. Journal of Discrete Mathematical Sciences and Cryptography 27, Nr. 2 (2024): 269–81. http://dx.doi.org/10.47974/jdmsc-1881.

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Cryptography, specifically encryption, plays a pivotal role in protecting data from unauthorized access. However, not all encryption methods are equally effective, as some exhibit vulnerabilities. This research proposing a novel encryption method that builds upon established techniques to enhance data security. The proposed method combines the strengths of the Festial encryption method and the Advanced Encryption Standard (AES) to create an algorithm that exhibits superior resistance against attacks. The proposed encryption method successfully mitigates vulnerabilities, demonstrating enhanced resilience against unauthorized access attempts and minimizing the potential for data leakage. By prioritizing security and advancing encryption technologies, it can effectively protect personal information, maintain data confidentiality and integrity, and establish a safer digital environment for individuals and organizations.

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Appenzeller, Arno, Moritz Leitner, Patrick Philipp, Erik Krempel und Jürgen Beyerer. „Privacy and Utility of Private Synthetic Data for Medical Data Analyses“. Applied Sciences 12, Nr. 23 (01.12.2022): 12320. http://dx.doi.org/10.3390/app122312320.

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The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). PETs attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is DP, which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case.

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Lobo-Vesga, Elisabet, Alejandro Russo und Marco Gaboardi. „A Programming Language for Data Privacy with Accuracy Estimations“. ACM Transactions on Programming Languages and Systems 43, Nr. 2 (Juli 2021): 1–42. http://dx.doi.org/10.1145/3452096.

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Differential privacy offers a formal framework for reasoning about the privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analysis results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages to ease the implementation of differentially private analyses. Even though these programming languages provide support for reasoning about privacy, most of them disregard reasoning about the accuracy of data analyses. To overcome this limitation, we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy, and their trade-offs. The distinguishing feature of DPella is a novel component that statically tracks the accuracy of different data analyses. To provide tight accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can calibrate the privacy parameters to meet the accuracy requirements, and vice versa.

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Dwork, Cynthia. „A firm foundation for private data analysis“. Communications of the ACM 54, Nr. 1 (Januar 2011): 86–95. http://dx.doi.org/10.1145/1866739.1866758.

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Bos, Joppe W., Kristin Lauter und Michael Naehrig. „Private predictive analysis on encrypted medical data“. Journal of Biomedical Informatics 50 (August 2014): 234–43. http://dx.doi.org/10.1016/j.jbi.2014.04.003.

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Aher, Ujjwala Bal, Amol A. Bhosle, Prachi Palsodkar, Swati Bula Patil, Nishchay Koul und Purva Mange. „Secure data sharing in collaborative network environments for privacy-preserving mechanisms“. Journal of Discrete Mathematical Sciences and Cryptography 27, Nr. 2-B (2024): 855–65. http://dx.doi.org/10.47974/jdmsc-1961.

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In today’s world where everything is linked, shared network settings are necessary to share info between companies. However, making sure that data is safe and private in these kinds of settings is very hard. This paper describes a new way to share data safely, focusing on privacy-protecting features that keep private data safe while making it easier for people to work together.To keep data safe at different steps of sharing and processing, the suggested answer uses a mix of cryptography, access controls, and anonymization techniques. Unauthorized access is stopped and privacy is kept by encrypting data both while it is being sent and while it is being stored. Access controls are also used to make sure that only people who are allowed to can see or change private data. These controls are based on rules that have already been set. Also, methods like differential privacy are used to make privacy even better by adding noise to question results. This stops individual records from being identified while still allowing useful analysis. Together, these methods make it possible to share data safely and privately, which encourages cooperation without jeopardizing accuracy or privacy.Overall, this method provides a complete framework for dealing with the tricky issues of data security and privacy in collaborative network settings. It lets businesses share information freely while still following strict privacy rules and keeping private data from getting into the wrong hands or being shared without permission.

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Dissertationen zum Thema "Private Data Analysis"

Puglisi, Silvia. „Analysis, modelling and protection of online private data“. Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/456205.

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Online communications generate a consistent amount of data flowing between users, services and applications. This information results from the interactions among different parties, and once collected, it is used for a variety of purposes, from marketing profiling to product recommendations, from news filtering to relationship suggestions. Understanding how data is shared and used by services on behalf of users is the motivation behind this work. When a user creates a new account on a certain platform, this creates a logical container that will be used to store the user's activity. The service aims to profile the user. Therefore, every time some data is created, shared or accessed, information about the user’s behaviour and interests is collected and analysed. Users produce this data but are unaware of how it will be handled by the service, and of whom it will be shared with. More importantly, once aggregated, this data could reveal more over time that the same users initially intended. Information revealed by one profile could be used to obtain access to another account, or during social engineering attacks. The main focus of this dissertation is modelling and analysing how user data flows among different applications and how this represents an important threat for privacy. A framework defining privacy violation is used to classify threats and identify issues where user data is effectively mishandled. User data is modelled as categorised events, and aggregated as histograms of relative frequencies of online activity along predefined categories of interests. Furthermore, a paradigm based on hypermedia to model online footprints is introduced. This emphasises the interactions between different user-generated events and their effects on the user’s measured privacy risk. Finally, the lessons learnt from applying the paradigm to different scenarios are discussed.
Las comunicaciones en línea generan una cantidad constante de datos que fluyen entre usuarios, servicios y aplicaciones. Esta información es el resultado de las interacciones entre diferentes partes y, una vez recolectada, se utiliza para una gran variedad de propósitos, desde perfiles de marketing hasta recomendaciones de productos, pasando por filtros de noticias y sugerencias de relaciones. La motivación detrás de este trabajo es entender cómo los datos son compartidos y utilizados por los servicios en nombre de los usuarios. Cuando un usuario crea una nueva cuenta en una determinada plataforma, ello crea un contenedor lógico que se utilizará para almacenar la actividad del propio usuario. El servicio tiene como objetivo perfilar al usuario. Por lo tanto, cada vez que se crean, se comparten o se accede a los datos, se recopila y analiza información sobre el comportamiento y los intereses del usuario. Los usuarios producen estos datos, pero desconocen cómo serán manejados por el servicio, o con quién se compartirán. O lo que es más importante, una vez agregados, estos datos podrían revelar, con el tiempo, más información de la que los mismos usuarios habían previsto inicialmente. La información revelada por un perfil podría utilizarse para obtener acceso a otra cuenta o durante ataques de ingeniería social. El objetivo principal de esta tesis es modelar y analizar cómo fluyen los datos de los usuarios entre diferentes aplicaciones y cómo esto representa una amenaza importante para la privacidad. Con el propósito de definir las violaciones de privacidad, se utilizan patrones que permiten clasificar las amenazas e identificar los problemas en los que los datos de los usuarios son mal gestionados. Los datos de los usuarios se modelan como eventos categorizados y se agregan como histogramas de frecuencias relativas de actividad en línea en categorías predefinidas de intereses. Además, se introduce un paradigma basado en hipermedia para modelar las huellas en línea. Esto enfatiza la interacción entre los diferentes eventos generados por el usuario y sus efectos sobre el riesgo medido de privacidad del usuario. Finalmente, se discuten las lecciones aprendidas de la aplicación del paradigma a diferentes escenarios.
Les comunicacions en línia generen una quantitat constant de dades que flueixen entre usuaris, serveis i aplicacions. Aquesta informació és el resultat de les interaccions entre diferents parts i, un cop recol·lectada, s’utilitza per a una gran varietat de propòsits, des de perfils de màrqueting fins a recomanacions de productes, passant per filtres de notícies i suggeriments de relacions. La motivació darrere d’aquest treball és entendre com les dades són compartides i utilitzades pels serveis en nom dels usuaris. Quan un usuari crea un nou compte en una determinada plataforma, això crea un contenidor lògic que s’utilitzarà per emmagatzemar l’activitat del propi usuari. El servei té com a objectiu perfilar a l’usuari. Per tant, cada vegada que es creen, es comparteixen o s’accedeix a les dades, es recopila i analitza informació sobre el comportament i els interessos de l’usuari. Els usuaris produeixen aquestes dades però desconeixen com seran gestionades pel servei, o amb qui es compartiran. O el que és més important, un cop agregades, aquestes dades podrien revelar, amb el temps, més informació de la que els mateixos usuaris havien previst inicialment. La informació revelada per un perfil podria utilitzar-se per accedir a un altre compte o durant atacs d’enginyeria social. L’objectiu principal d’aquesta tesi és modelar i analitzar com flueixen les dades dels usuaris entre diferents aplicacions i com això representa una amenaça important per a la privacitat. Amb el propòsit de definir les violacions de privacitat, s’utilitzen patrons que permeten classificar les amenaces i identificar els problemes en què les dades dels usuaris són mal gestionades. Les dades dels usuaris es modelen com esdeveniments categoritzats i s’agreguen com histogrames de freqüències relatives d’activitat en línia en categories predefinides d’interessos. A més, s’introdueix un paradigma basat en hipermèdia per modelar les petjades en línia. Això emfatitza la interacció entre els diferents esdeveniments generats per l’usuari i els seus efectes sobre el risc mesurat de privacitat de l’usuari. Finalment, es discuteixen les lliçons apreses de l’aplicació del paradigma a diferents escenaris.

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Alborch, escobar Ferran. „Private Data Analysis over Encrypted Databases : Mixing Functional Encryption with Computational Differential Privacy“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAT003.

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Dans l'actuelle société numérique, les données dominent le monde. Associées la plupart du temps à des individus, leur exploitation doit respecter la vie privée de ces derniers. Cette contrainte a donné naissance au paradigme de confidentialité différentielle, qui permet de protéger les individus lors de requêtes sur des bases contenant des données les concernant. Mais avec l'émergence du "cloud computing'', il devient nécessaire de prendre en compte la confidentialité du stockage de ces dernières dans le cloud, en utilisant du chiffrement. Cette thèse étudie comment assurer à la fois la confidentialité et le respect de la vie privée de ces bases de données externalisées en combinant deux primitives : la confidentialité différentielle calculatoire et le chiffrement fonctionnel. Dans un premier temps, nous étudions les liens entre la confidentialité différentielle calculatoire et le chiffrement fonctionnel pour des fonctions aléatoires d'un point de vue générique. Nous analysons la confidentialité dans un cadre où un analyste malicieux peut accéder aux données chiffrées stockés dans un serveur soit par corruption soit par brèche de sécurité, et nous prouvons qu'un schéma de chiffrement fonctionnel aléatoire sûr et pour la famille de fonctions appropriée garantie la confidentialité différentielle calculatoire du système. Dans second temps, nous construisons des schémas de chiffrement fonctionnel aléatoire pour certaines familles de fonctions utiles, et nous les prouvons sûrs dans le modèle standard sous des hypothèses très étudiées. Les familles de fonctions que nous étudions sont les fonctions linéaires, utilisées par exemple pour requêtes de comptage, des histogrammes et régressions linéaires, et les fonctions quadratiques, utilisées par exemple pour régressions quadratiques et tests d'hypothèses. Les schémas proposés sont utilisés avec le premier résultat pour construire des bases des données chiffrées pour fonctions linéaires et quadratiques respectivement. Finalement, nous implémentons les deux schémas de chiffrement fonctionnel pour analyser leur efficacité. Cela montre que nos constructions sont pratiques pour des bases de données avec 1 000 000 entrées pour des requêtes linéaires et des bases de données avec 10 000 entrées pour des requêtes quadratiques
In our current digitalized society, data is ruling the world. But as it is most of the time related to individuals, its exploitation should respect the privacy of the latter. This issue has raised the differential privacy paradigm, which permits to protect individuals when querying databases containing data about them. But with the emergence of cloud computing, it is becoming increasingly necessary to also consider the confidentiality of "on-cloud'' storage confidentiality of such vast databases, using encryption techniques. This thesis studies how to provide both privacy and confidentiality of such outsourced databases by mixing two primitives: computational differential privacy and functional encryption. First, we study the relationship between computational differential privacy and functional encryption for randomized functions in a generic way. We analyze the privacy of the setting where a malicious analyst may access the encrypted data stored in a server, either by corrupting or breaching it, and prove that a secure randomized functional encryption scheme supporting the appropriate family of functions guarantees the computational differential privacy of the system. Second, we construct efficient randomized functional encryption schemes for certain useful families of functions, and we prove them secure in the standard model under well-known assumptions. The families of functions considered are linear functions, used for example in counting queries, histograms and linear regressions, and quadratic functions, used for example in quadratic regressions and hypothesis testing. The schemes built are then used together with the first result to construct encrypted databases for their corresponding family of queries. Finally, we implement both randomized functional encryption schemes to analyze their efficiency. This shows that our constructions are practical for databases with up to 1 000 000 entries in the case of linear queries and databases with up to 10 000 database entries in the case of quadratic queries

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Amirbekyan, Artak. „Protocols and Data Structures for Knowledge Discovery on Distributed Private Databases“. Thesis, Griffith University, 2007. http://hdl.handle.net/10072/367447.

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Data mining has developed many techniques for automatic analysis of today’s rapidly collected data. Yahoo collects 12 TB daily of query logs and this is a quarter of what Google collects. For many important problems, the data is actually collected in distributed format by different institutions and organisations, and it can relate to businesses and individuals. The accuracy of knowledge that data mining brings for decision making depends on considering the collective datasets that describe a phenomenon. But privacy, confidentiality and trust emerge as major issues in the analysis of partitioned datasets among competitors, governments and other data holders that have conflicts of interest. Managing privacy is of the utmost importance in the emergent applications of data mining. For example, data mining has been identified as one of the most useful tools for the global collective fight on terror and crime [80]. Parties holding partitions of the database are very interested in the results, but may not trust the others with their data, or may be reluctant to release their data freely without some assurances regarding privacy. Data mining technology that reveals patterns in large databases could compromise the information that an individual or an organisation regards as private. The aim is to find the right balance between maximising analysis results (that are useful for each party) and keeping the inferences that disclose private information about organisation or individuals at a minimum. We address two core data analysis tasks, namely clustering and regression. For these to be solvable in the privacy context, we focus on the protocol’s efficiency and practicality. Because associative queries are central to clustering (and to many other data mining tasks), we provide protocols for privacy-preserving knear neighbour (k-NN) queries. Our methods improve previous methods for k-NN queries in privacy-preserving data-mining (which are based on Fagin’s A0 algorithm) because we do leak at least an order of magnitude less candidates and we achieve logarithmic performance on average. The foundations of our methods for k-NN queries are two pillars, firstly data structures and secondly, metrics. This thesis provides protocols for privacy-preserving computation of various common metrics and for construction of necessary data structures. We present here new algorithms for secure-multiparty-computation of some basic operations (like a new solution for Yao’s comparison problem and new protocols to perform linear algebra, in particular the scalar product). These algorithms will be used for the construction of protocols for different metrics (we provide protocols for all Minkowski metrics, the cosine metrics and the chessboard metric) and for performing associative queries in the privacy context. In order to be efficient, our protocols for associative queries are supported by specific data structures. Thus, we present the construction of privacy-preserving data structures like R-Trees [42, 7], KD-Trees [8, 53, 33] and the SASH [8, 60]. We demonstrate the use of all these tools, and we provide a new version of the well known clustering algorithm DBSCAN [42, 7]. This new version is now suitable for applications that demand privacy. Similarly, we apply our machinery and provide new multi-linear regression protocols that are now suitable for privacy applications. Our algorithms are more efficient than earlier methods and protocols. In particular, the cost associated with ensuring privacy provides only a linear-cost overhead for most of the protocols presented here. That is, our methods are essentially as costly as concentrating all the data in one site, performing the data-mining task, and disregarding privacy. However, in some cases we make use of a third-trusted party. This is not a problem when more than two parties are involved, since there is always one party that can act as the third.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
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Nguyen, Mai Phuong. „Contribution of private healthcare to universal health coverage: an investigation of private over public health service utilisation in Vietnam“. Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/225903/1/Mai%20Phuong_Nguyen_Thesis.pdf.

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Achievement of Universal Health Coverage (UHC) is a desirable goal for all countries. Complementary public and private services are essential. This study examined factors that influence consumer choice for private and public health care services in Vietnam. Thirty senior healthcare professionals were interviewed and secondary data on over 35,000 episodes of healthcare gathered during national health surveys in households were analyzed. For Vietnam and similar low and middle-income countries to achieve UHC, it is necessary to overcome incomplete social health insurance coverage, variable quality of private and public health services, unregulated quality in advertising and inefficient competition between sectors.

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Karim, Martia. „Determinants of Venture Capital Investments : A panel data analysis across regions in the United Kingdom“. Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-40179.

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Venture Capital is an important theme in economic research as a growing intermediate in the financing of new or growing young firms. In Europe, it is the United Kingdom that is the leading country with the highest amount of venture capital activity. However, there is a wide spatial distribution of venture capital across the regions of the United Kingdom where London and the South East alone pulled nearly 60% of venture capital in 2013. This paper focuses on a cross-regional study with the selected regions of the United Kingdom: Scotland, England, Wales, and Northern Ireland. The purpose to investigate the relationship between economic growth, research & development expenditure, and population density with total venture capital investments during the time period 2006 – 2016. The aim is to contribute to existing literature on determinants of venture capital with evidence from the United Kingdom. Using a fixed effect model, we can establish a positive relationship between population density and total venture capital invested. Economic growth and gross expenditure on research & development did not yield any significant result.

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Habibovic, Sanel. „VIRTUAL PRIVATE NETWORKS : An Analysis of the Performance in State-of-the-Art Virtual Private Network solutions in Unreliable Network Conditions“. Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17844.

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This study aimed to identify the differences between state-of-the-art VPN solutions on different operating systems. It was done because a novel VPN protocol is in the early stages of release and a comparison of it, to other current VPN solutions is interesting. It is interesting because current VPN solutions are well established and have existed for a while and the new protocol stirs the pot in the VPN field. Therefore a contemporary comparison between them could aid system administrators when choosing which VPN to implement. To choose the right VPN solution for the occasion could increase performance for the users and save costs for organizations who wish to deploy VPNs. With the remote workforce increasing issues of network reliability also increases, due to wireless connections and networks beyond the control of companies. This demands an answer to the question how do VPN solutions differ in performance with stable and unstable networks? This work attempted to answer this question. This study is generally concerning VPN performance but mainly how the specific solutions perform under unreliable network conditions.It was achieved by researching past comparisons of VPN solutions to identify what metrics to analyze and which VPN solutions have been recommended. Then a test bed was created in a lab network to control the network when testing, so the different VPN implementations and operating systems have the same premise. To establish baseline results, performance testing was done on the network without VPNs, then the VPNs were tested under reliable network conditions and then with unreliable network conditions. The results of that were compared and analyzed. The results show a difference in the performance of the different VPNs, also there is a difference on what operating system is used and there are also differences between the VPNs with the unreliability aspects switched on. The novel VPN protocol looks promising as it has overall good results, but it is not conclusive as the current VPN solutions can be configured based on what operating system and settings are chosen. With this set-up, VPNs on Linux performed much better under unreliable network conditions when compared to setups using other operating systems. The outcome of this work is that there is a possibility that the novel VPN protocol is performing better and that certain combinations of VPN implementation and OS are better performing than others when using the default configuration. This works also pointed out how to improve the testing and what aspects to consider when comparing VPN implementations.

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Ciccarelli, Armand. „An analysis of the impact of wireless technology on public vs. private traffic data collection, dissemination and use“. Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8817.

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Thesis (M.C.P. and S.M.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2001.
Includes bibliographical references (leaves 151-154).
The collection of data concerning traffic conditions (e.g., incidents, travel times, average speed, traffic volumes, etc.) on roadways has traditionally been carried out by those public entities charged with managing traffic flow, responding to incidents, and maintaining the surface of the roadway. Pursuant to this task, public agencies have employed inductive loop detectors, closed circuit television cameras, technology for tracking electronic toll tags, and other surveillance devices, in an effort to monitor conditions on roads within their jurisdictions. The high cost of deploying and maintaining this surveillance equipment has precluded most agencies from collecting data on roads other than freeways and important arterials. In addition, the "point" nature of most commonly utilized surveillance equipment limits both the variety of data available for analysis, as well as its overall accuracy. Consequently, these problems have limited the usefulness of this traffic data, both to the public agencies collecting it, as well as private entities who would like to use it as a resource from which they can generate fee-based traveler information services. Recent Federal Communications Commission (FCC) mandates concerning E-911 have led to the development of new technologies for tracking wireless devices (i.e., cellular phones). Although developed to assist mobile phone companies in meeting the FCC's E-911 mandate, a great deal of interest has arisen concerning their application to the collection of traffic data. That said, the goal of this thesis has been to compare traditional traffic surveillance technologies' capabilities and effectiveness with that of the wireless tracking systems currently under development. Our technical research indicates that these newly developed tracking technologies will eventually be able to provide wider geographic surveillance of roads at less expense than traditional surveillance equipment, as well as collect traffic information that is currently unavailable. Even so, our overall conclusions suggest that due to budgetary, institutional, and/or political constraints, some organizations may find themselves unable to procure this high quality data. Moreover, we believe that even those organizations (both public and private) that find themselves in a position to procure data collected via wireless tracking technology should first consider the needs of their "customers," the strength of the local market for traffic data, and their organization's overall mission, prior to making a final decision.
by Armand J. Ciccarelli, III.
M.C.P.and S.M.

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Aronsson, Arvid, und Daniel Falkenström. „The Effects of Capital Income Taxation on Consumption : Panel data analysis of the OECD countries“. Thesis, Jönköping University, IHH, Nationalekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-52920.

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This thesis investigates if the tax rate on dividend income has a significant effect on private consumption expenditure. This is done through a panel study on 36 OECD countries during the period 2000-2019. Regressions using differenced data and several control variables are used. The results are to some extent in line with previous empirical work studying the effects of tax changes on consumption. The results indicate that the taxation of capital income in the form of the overall tax rate on dividend income does not have a significant effect on private consumption expenditure. The theoretical mechanism deemed most likely to be in effect is tax planning since contradictory results are obtained regarding the effects of other tax rates in the form of taxes on labour income and VAT on private consumption expenditure.

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Shimada, Hideki. „Econometric Analysis of Social Interactions and Economic Incentives in Conservation Schemes“. Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263702.

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Lanjouw, Jean Olson. „The private value of patent rights : a dynamic programming and game theoretic analysis of West German patent renewal data, 1953-1988“. Thesis, London School of Economics and Political Science (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527825.

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Empirical estimates of the private value of patent protection are found for four technology areas - computers, textiles, combustion engines and pharmaceuticals - using new patent renewal data of West German patents collected for the period 1953-1988. In Germany, a patentee must pay an annual renewal fee to keep his patent in force. Two dynamic discrete choice models of optimal renewal decisions are developed and used. in conjunction with observed hazard proportions and renewal fee schedules, to estimate the returns to protection. Differences in value across technology, nationality of inventor and time are explored both non-parametrically and parametrically within a deterministic framework. A stochastic formulation of the model, which allows both for learning about the innovation and market and for the possibility of infringements, is estimated using a minimum distance simulation estimator. The evolution of the distribution of returns over the life of a group of patents is calculated for each technology. Results indicate that learning is completed after 6 years, that obsolescence is rapid, and that the distributions of patent value are very skewed. Research and development (R&D) expenditures for each technology area are calculated and patent protection as an implicit subsidy to investment in R&D is discussed. Patent protection is valuable only when there are potential competitors for the use of an innovation. Patent rights must be defended. A game theoretic analysis of litigation explores how these facts influence the decision whether to apply for and keep a patent in force and, in tum, the relationship between the distribution of patent value and that of the underlying innovation. Implications for renewal behaviour are derived from the analysis and the data suggests that the level of potential competition does affect the value of protection. Consideration is given to how these findings bear on the interpretation of empirical estimates of patent value as indicators of innovation.

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Plotkin, Stephen. Private well water quality data bank feasibility analysis for Massachusetts. Amherst, MA: University of Massachusetts Private Well Water Quality Data Bank Feasiblity Study, 1989.

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Burnell, Derek. Investment expectations: A unit record analysis of data from the private new capital expenditure survey. Canberra: Australian Bureau of Statistics, 1993.

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Ayele, Gashaw T. Do tax structures optimize private fixed investment in sub-Saharan Africa?: A data envelopment analysis. Addis Ababa, Ethiopia: The Horn Economic and Social Policy Institute, 2015.

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Mario Viola de Azevedo Cunha. Market Integration Through Data Protection: An Analysis of the Insurance and Financial Industries in the EU. Dordrecht: Springer Netherlands, 2013.

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Da cheng qi ye yan jiu yuan. Min ying jing ji gai bian Zhongguo: Gai ge kai fang 40 nian min ying jing ji zhu yao shu ju jian ming fen xi = Private economy changes China : concise analysis of private economy data 40-years reform and opening up. Beijing Shi: She hui ke xue wen xian chu ban she, 2018.

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Mal'shina, N., und Andrey Garnov. MODERN PRINCIPLES ANALYSIS OF RESOURCE FLOWS IN CRISIS CONDITIONS: CULTURE AND CREATIVE INDUSTRY. xxu: Academus Publishing, 2020. http://dx.doi.org/10.31519/978-1-4946-0018-1.

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The culture industry should become attractive for capital investment through the development of organizational-economic mechanisms of support in the form of integrated structures as well as through the development of mechanisms of its financing: systems of culture multi-channel backing and state-private partnership which would allow to create prerequisites for the appearance and implementation of new ideas and projects in the culture field, contributing to culture sphere formation as a full-fledged source of state income. As a result of this project implementation, original new fundamental theoretical assumptions and empirical data in the culture industry field and the regional development will be obtained. The project focuses on the study of fundamental basics research of the culture industry functioning, solves scientific problems of the culture industry effectiveness evaluation and direction justification of its support and funding; there are being worked out strategies and mechanisms of the culture industry efficiency development and increase in accordance with the characteristics and needs of regional economies.

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Lut, Yuliia. Privacy-Aware Data Analysis: Recent Developments for Statistics and Machine Learning. [New York, N.Y.?]: [publisher not identified], 2022.

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Bruijn, Hans. The Governance of Privacy. NL Amsterdam: Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789463729673.

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We can hardly underestimate the importance of privacy in our data-driven world. Privacy breaches are not just about disclosing information. Personal data is used to profile and manipulate us – sometimes on such a large scale that it affects society as a whole. What can governments do to protect our privacy? In The Governance of Privacy Hans de Bruijn first analyses the complexity of the governance challenge, using the metaphor of a journey. At the start, users have strong incentives to share data. Harvested data continue the journey that might lead to a privacy breach, but not necessarily – it can also lead to highly valued services. That is why both preparedness at the start of the journey and resilience during the journey are crucial to privacy protection. The book then explores three strategies to deal with governments, the market, and society. Governments can use the power of the law; they can exploit the power of the market by stimulating companies to compete on privacy; and they can empower society, strengthening its resilience in a data-driven world.

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Sloot, Bart, und Aviva Groot, Hrsg. The Handbook of Privacy Studies. NL Amsterdam: Amsterdam University Press, 2018. http://dx.doi.org/10.5117/9789462988095.

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The Handbook of Privacy Studies is the first book in the world that brings together several disciplinary perspectives on privacy, such as the legal, ethical, medical, informatics and anthropological perspective. Privacy is in the news almost every day: mass surveillance by intelligence agencies, the use of social media data for commercial profit and political microtargeting, password hacks and identity theft, new data protection regimes, questionable reuse of medical data, and concerns about how algorithms shape the way we think and decide. This book offers interdisciplinary background information about these developments and explains how to understand and properly evaluate them. The book is set up for use in interdisciplinary educational programmes. Each chapter provides a structured analysis of the role of privacy within that discipline, its characteristics, themes and debates, as well as current challenges. Disciplinary approaches are presented in such a way that students and researchers from every scientific background can follow the argumentation and enrich their own understanding of privacy issues.

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Munir, Abu Bakar. Privacy and data protection: A comparative analysis with special reference to the Malaysian proposed law. Petaling Jaya, Selangor, Malaysia: Sweet & Maxwell Asia, 2002.

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Zhu, Tianqing, Gang Li, Wanlei Zhou und Philip S. Yu. „Differentially Private Data Analysis“. In Advances in Information Security, 49–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62004-6_6.

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Weiß, Stefanie. „Data analysis and results“. In Determinants of Private Label Attitude, 67–79. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08672-5_4.

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Raskhodnikova, Sofya, und Adam Smith. „Private Analysis of Graph Data“. In Encyclopedia of Algorithms, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_549-1.

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Nissim, Kobbi. „Private Data Analysis via Output Perturbation“. In Privacy-Preserving Data Mining, 383–414. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5_16.

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Pelletier, Mathilde, Nicolas Saunier und Jerome Le Ny. „Differentially Private Analysis of Transportation Data“. In Privacy in Dynamical Systems, 131–55. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0493-8_7.

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Wang, Ke, Benjamin C. M. Fung und Guozhu Dong. „Integrating Private Databases for Data Analysis“. In Intelligence and Security Informatics, 171–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427995_14.

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Haire, M. J., und E. H. Gift. „A Guide for Reliability and Maintainability Data Acquisition“. In Risk Analysis in the Private Sector, 377–84. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2465-2_29.

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Thakurta, Abhradeep. „Beyond Worst Case Sensitivity in Private Data Analysis“. In Encyclopedia of Algorithms, 192–99. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_547.

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Thakurta, Abhradeep. „Beyond Worst Case Sensitivity in Private Data Analysis“. In Encyclopedia of Algorithms, 1–8. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_547-1.

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Dwork, Cynthia, Frank McSherry, Kobbi Nissim und Adam Smith. „Calibrating Noise to Sensitivity in Private Data Analysis“. In Theory of Cryptography, 265–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11681878_14.

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Konferenzberichte zum Thema "Private Data Analysis"

Wang, Longfeng, Yutong Liu, Mingyan Wang, Qinghua Chen, Fengting Wang und Jiawei Chen. „Human Travel Behavior Analysis Using Private Car Data“. In 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 410–14. IEEE, 2024. http://dx.doi.org/10.1109/aiea62095.2024.10692794.

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Zhou, Haiyan. „ASP Cluster analysis of humanistic quality evaluation data for private college students using NET technology and K-Means algorithm“. In 2024 International Conference on Informatics Education and Computer Technology Applications (IECA), 15–20. IEEE, 2024. http://dx.doi.org/10.1109/ieca62822.2024.00010.

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Zainab, Syeda Sana e., und Tahar Kechadi. „Sensitive and Private Data Analysis“. In ICFNDS '19: 3rd International Conference on Future Networks and Distributed Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341325.3342002.

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Takabi, Hassan, Samir Koppikar und Saman Taghavi Zargar. „Differentially Private Distributed Data Analysis“. In 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC). IEEE, 2016. http://dx.doi.org/10.1109/cic.2016.038.

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Hossain, Md Tamjid, Shahriar Badsha und Haoting Shen. „Privacy, Security, and Utility Analysis of Differentially Private CPES Data“. In 2021 IEEE Conference on Communications and Network Security (CNS). IEEE, 2021. http://dx.doi.org/10.1109/cns53000.2021.9705022.

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Wang, Ning. „Data-dependent differentially private publication of horizontally partitioned data“. In International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), herausgegeben von Fengjie Cen, Christos J. Bouras und Razali Yaakob. SPIE, 2022. http://dx.doi.org/10.1117/12.2627689.

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Zhu, Keyu, Ferdinando Fioretto und Pascal Van Hentenryck. „Post-processing of Differentially Private Data: A Fairness Perspective“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/559.

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Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees. Post-processing is routinely applied in data-release applications, including census data, which are then used to make allocations with substantial societal impacts. This paper shows that post-processing causes disparate impacts on individuals or groups and analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions, such as the allocation of funds informed by US Census data. In the first setting, the paper proposes tight bounds on the unfairness for traditional post-processing mechanisms, giving a unique tool to decision makers to quantify the disparate impacts introduced by their release. In the second setting, this paper proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics, either reducing fairness issues substantially or reducing the cost of privacy. The theoretical analysis is complemented with numerical simulations on Census data.

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Li, Zhenju, Xuejun Li, Tao Liu, Sheng Yang und Jianwei Xie. „A preliminary data transfer model for private remote sensing cloud“. In 2016 IEEE International Conference on Big Data Analysis (ICBDA). IEEE, 2016. http://dx.doi.org/10.1109/icbda.2016.7509816.

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Pustozerova, Anastasia, Jan Baumbach und Rudolf Mayer. „Differentially Private Federated Learning: Privacy and Utility Analysis of Output Perturbation and DP-SGD“. In 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023. http://dx.doi.org/10.1109/bigdata59044.2023.10386466.

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Nissim, Kobbi, Sofya Raskhodnikova und Adam Smith. „Smooth sensitivity and sampling in private data analysis“. In the thirty-ninth annual ACM symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1250790.1250803.

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Berichte der Organisationen zum Thema "Private Data Analysis"

Young, Andrew Young, Andrew J. Zahuranec Zahuranec, Michelle Winowatan Winowatan und Stefaan G. Verhulst Verhulst. LEVERAGING PRIVATE DATA FOR PUBLIC GOOD: A Descriptive Analysis and Typology of Existing Practices. GovLab, Oktober 2019. http://dx.doi.org/10.15868/socialsector.40380.

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McGill, Karis, und Eleanor Turner. Return on Investment Analysis of Private Sector Facilitation Funds for Rwandan Agribusinesses. RTI Press, August 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0042.2008.

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This study analyzes the return on investment for an agribusiness facilitation fund implemented in Rwanda. Combining project monitoring data with supplementary surveys and interviews of recipient agribusinesses, we find a positive return on investment in terms of farmer income generated per dollar spent by the US government. To determine the commercial viability of the investments, we estimate the payback period and find the median time it will take a firm to recoup the entire investment through profits is 3.7 years. We estimate the net present value of the entire fund portfolio to be $12.5 million. These estimates rely on conservative assumptions and likely underrepresent the profitability of the investments. Given the positive returns and commercial viability of the agribusinesses, we examine the fund’s role as a first step to “graduate” firms toward investment readiness. Although three firms did access equity investment, we find that the majority of the businesses in the portfolio do not meet investor requirements for deal size and management capacity and are more appropriately financed by commercial lenders. We conclude with recommendations for the implementation and measurement of similar funds.

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Day, Christopher M., Howell Li, Sarah M. L. Hubbard und Darcy M. Bullock. Observations of Trip Generation, Route Choice, and Trip Chaining with Private-Sector Probe Vehicle GPS Data. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317368.

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This paper presents an exploratory study of GPS data from a private-sector data provider for analysis of trip generation, route choice, and trip chaining. The study focuses on travel to and from the Indianapolis International Airport. GPS data consisting of nearly 1 billion waypoints for 12 million trips collected over a 6-week period in the state of Indiana. Within this data, there were approximately 10,000 trip records indicating travel to facilities associated with the Indianapolis airport. The analysis is based the matching of waypoints to geographic areas that define the extents of roadways and various destinations. A regional analysis of trip ends finds that travel demand for passenger services at the airport extends across a region spanning about 950 km. Local travel between land uses near the airport is examined by generation of an origin-destination matrix, and route choice between the airport and downtown Indianapolis is studied. Finally, the individual trips are scanned to identify trip chaining behavior. Several observations are made regarding these dynamics from the data. There is some sample bias (types of vehicles) and opportunities to further refine some of the land use definitions, but the study results suggest this type of data will provide a new frontier for characterizing travel demand patterns at a variety of scales.

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Smith, Paul N., David R. J. Gill, Michael J. McAuliffe, Catherine McDougall, James D. Stoney, Christopher J. Vertullo, Christopher J. Wall et al. Analysis of State and Territory Health Data: Supplementary Report. Australian Orthopaedic Association, Oktober 2023. http://dx.doi.org/10.25310/ixwe4642.

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Data presented in this report are for both the public and private hospital sector and have been obtained from State and Territory Health Departments for specific ICD-10-AM codes relating to hip, knee, shoulder, elbow, wrist, ankle and spinal disc replacement. Data for each state are presented individually with the exception of data for the Australian Capital Territory (ACT) and Northern Territory (NT) which have been combined. Wrist replacement procedures for South Australia (SA) and Western Australia (WA) have also been combined due to small numbers. This report provides information on joint replacement for the financial year 1 July 2021 to 30 June 2022. It also includes long term data from as early as 1994/95 which provides a national perspective on the changing rates of hip and knee replacement since that time. This Report is one of 16 supplementary reports to complete the AOANJRR Annual Report for 2023. Information on the background, purpose, aims, benefits and governance of the Registry can be found in the Introductory chapter of the 2023 Hip, Knee and Shoulder Arthroplasty Annual Report. The Registry data quality processes including data collection, validation and outcomes assessment, are provided in detail in the Data Quality section of the introductory chapter of the 2023 Hip, Knee and Shoulder Arthroplasty Annual Report: https://aoanjrr.sahmri.com/annual-reports-2023.

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Coombs, Elizabeth, Orazio J. Bellettini Cedeño und Paul E. Carrillo. Stay Public or Go Private?: A Comparative Analysis of Water Services between Quito and Guayaquil. Inter-American Development Bank, August 2007. http://dx.doi.org/10.18235/0011284.

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This paper computes several indicators of water coverage, quality, and prices in Ecuador's two largest cities: Quito and Guayaquil- both before and after the privatization of water services in Guayaquil. The type of data sources that are used make it possible to specifically control for income and, thus, to evaluate changes in water provision, particularly among the poor. These indicators provide useful information about how certain water-related services have changed over time and facilitate evaluating the performance of each company. It should be emphasized, however, that such estimates cannot be used to identify the causal effects of the privatization of water provision. In particular, differences in a) before-concession water-coverage trends, c) rural-to-urban migration patterns, and c) other idiosyncratic institutional characteristics between these two cities, suggest that Quito may not be a suitable control group for identifying the casual effects of privatization.

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Lewis, Peter L., David RJ Gill, Michael J. McAuliffe, Catherine McDougall, James D. Stoney, Christopher J. Vertullo, Christopher J. Wall et al. Analysis of State and Territory Health Data: 2024 Supplementary Report. Australian Orthopaedic Association, Oktober 2024. http://dx.doi.org/10.25310/tgxs9590.

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Data presented in this report are for both the public and private hospital sector and have been obtained from State and Territory Health Departments for specific ICD-10-AM codes relating to hip, knee, shoulder, elbow, wrist, ankle and spinal disc replacement. The AOANJRR, in collaboration with service provider SAHMRI, has curated and produced this report. Responsibility for data accuracy and collection remains with the jurisdictional departments of health. Data for each state are presented individually with the exception of data for the Australian Capital Territory (ACT) and Northern Territory (NT) which have been combined. Wrist replacement procedures for South Australia (SA) and Western Australia (WA) have also been combined due to small numbers. This report provides information on joint replacement for the financial year 1 July 2022 to 30 June 2023. It also includes long term data from as early as 1994/95 which provides a national perspective on the changing rates of hip and knee replacement since that time. This Report is one of 14 supplementary reports to complete the AOANJRR Annual Report for 2024. Information on the background, purpose, aims, benefits and governance of the Registry can be found in the Introductory chapter of the 2024 Hip, Knee and Shoulder Arthroplasty Annual Report. The Registry data quality processes including data collection, validation and outcomes assessment, are provided in detail in the Data Quality section of the introductory chapter of the 2024 Hip, Knee and Shoulder Arthroplasty Annual Report: https://aoanjrr.sahmri.com/annual-reports-2024.

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Jalles, João Tovar, Donghyun Park und Irfan Qureshi. ADB Economics Working Paper Series 737: Public versus Private Investment Multipliers in Emerging Market and Developing Economies: Cross-Country Analysis with a Focus on Asia. Asian Development Bank, August 2024. http://dx.doi.org/10.22617/wps240372-2.

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This paper introduces new measures of investment shocks based on cyclically adjusted investment data to examine the effect of public and private investment shocks on growth. The results suggest that public investment shocks play a much greater role in boosting economic growth in comparison with private investment shocks. In emerging market and developing economies, including those in Asia, the growth response to investment shocks is positive and much stronger during recessions compared with economic expansions. Investment shocks also stimulate private investment and private consumption.

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Aguiar, Angel, Caitlyn Carrico, Thomas Hertel, Zekarias Hussein, Robert McDougall und Badri Narayanan. Extending the GTAP framework for public procurement analysis. GTAP Working Paper, August 2016. http://dx.doi.org/10.21642/gtap.wp82.

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This paper extends the GTAP framework to aid in the analysis of changes to public procurement policies. In terms of data developments, government investment demand data is estimated for each of the 57 GTAP Commodities in the 140 regions of version 9. In addition, the origin of imports by end use (i.e., for firms, private consumption, government consumption, and investment) is determined following the recent literature. Another layer of valuation is also introduced, which captures the preferences towards domestic production. In terms of model extensions, there is a new nest in the production structure that allows for different procurement regimes, and the origin of imports by agents’ end use is incorporated. We illustrate this framework by simulating the impact of a hypothetical reduction in the domestic preference in one of the newly introduced procurement regimes. Future work should focus on estimation of these domestic preference margins.

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Tawfik, Aly, und Utsav Shah. Analysis of Freight Movements in the San Joaquin Valley. Mineta Transportation Institute, Februar 2023. http://dx.doi.org/10.31979/mti.2023.2131.

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Freight transportation plays a primary role in supply chains, costs, and availability of goods and is a major part of the economy. This study identifies, assesses, and utilizes different data sources to uncover and understand the patterns and movements of different types of freight in the San Joaquin Valley’s (SJV's) different counties. The San Joaquin Valley region consists of eight counties: San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, and Tulare. This research explored some major datasets with freight information, such as Global Trade Atlas (GTA), Port Imports and Exports Reporting Systems (PIERS), and Streetlight data insights for the year 2019 (to explore movements of freight pre-COVID-19). The primary software programs used for this analysis include MS Excel, MS Access, ArcGIS, and StreetLight InSight. This research investigated all modes of freight transportation (air, water, rail, and road) for domestic and international trade. This research’s findings demonstrate the strengths and limitations of different data sources for understanding freight movements in the San Joaquin Valley. These findings should be valuable for different government and private agencies for various use cases such as developing transportation infrastructure, freight business, and environment assessments.

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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez und Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, Juni 2024. http://dx.doi.org/10.61152/hejw8941https://www.orangesparkleball.com/innovation-library-blog/2024/5/30/sunbelt2024-network-analysis-of-a-mobility-ecosystem-in-detroit-mi.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress. Methods The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software. Findings The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network. These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.

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