Agrupamiento jerárquico para la detección de condiciones de tráfico anómalo en subestaciones de energía

The IEC 61850 standard has contributed significantly to the substation management and automation process by incorporating the advantages of communications networks into the operation of power substations. However, this modernization process also involves new challenges in other areas. For example, in the field of security, several academic works have shown that the same attacks used in computer networks (DoS, Sniffing, Tampering, Spoffing among others), can also compromise the operation of a substation. This article evaluates the applicability of hierarchical clustering algorithms and statistical type descriptors (averages), in the identification of anomalous patterns of traffic in communication networks for power substations based on the IEC 61850 standard. The results obtained show that, using a hierarchical algorithm with Euclidean distance proximity criterion and simple link grouping method, a correct classification is achieved in the following operation scenarios: 1) Normal traffic, 2) IED disconnection, 3) Network discovery attack, 4) DoS attack, 5) IED spoofing attack and 6) Failure on the high voltage line. In addition, the descriptors used for the classification proved equally effective with other unsupervised clustering techniques such as K-means (partitional-type clustering), or LAMDA (diffuse-type clustering).El estándar IEC 61850 ha contribuido notablemente con el proceso de gestión y automatización de las subestaciones, al incorporar las ventajas de las redes de comunicaciones en la operación de las subestaciones de energía. Sin embargo, este proceso de modernización también involucra nuevos desafíos en otros campos. Por ejemplo, en el área de la seguridad, diversos trabajos académicos han puesto en evidencia que la operación de una subestación también puede ser comprometida por los mismos ataques utilizados en las redes de cómputo (DoS, Sniffing, Tampering, Spoffing entre otros). Este artículo evalúa la aplicabilidad de los algoritmos de agrupamiento no supervisado de tipo jerárquico y el uso de descriptores de tipo estadístico (promedios), en la identificación de patrones de tráfico anómalo en redes de comunicación para subestaciones eléctricas basadas en el estándar IEC 61850. Los resultados obtenidos demuestran que, utilizando un algoritmo jerárquico con criterio de proximidad distancia Euclidiana y método de agrupación vínculo simple, se logra una correcta clasificación de los siguientes escenarios de operación: 1) Tráfico normal, 2) Desconexión de dispositivo IED, 3) Ataque de descubrimiento de red, 4) Ataque de denegación de servicio, 5) Ataque de suplantación de IED y 6) Falló en la línea de alta tensión. Además, los descriptores utilizados para la clasificación demostraron ser robustos al lograrse idénticos resultados con otras técnicas de agrupamiento no supervisado de tipo particional como K-medias o de tipo difuso como LAMDA (Learning Algorithm Multivariable and Data Analysis)

How Much Training Data Is Enough? A Case Study for HTTP Anomaly-Based Intrusion Detection

Most anomaly-based intrusion detectors rely on models that learn from training datasets whose quality is crucial in their performance. Albeit the properties of suitable datasets have been formulated, the influence of the dataset size on the performance of the anomaly-based detector has received scarce attention so far. In this work, we investigate the optimal size of a training dataset. This size should be large enough so that training data is representative of normal behavior, but after that point, collecting more data may result in unnecessary waste of time and computational resources, not to mention an increased risk of overtraining. In this spirit, we provide a method to find out when the amount of data collected at the production environment is representative of normal behavior in the context of a detector of HTTP URI attacks based on 1-grammar. Our approach is founded on a set of indicators related to the statistical properties of the data. These indicators are periodically calculated during data collection, producing time series that stabilize when more training data is not expected to translate to better system performance, which indicates that data collection can be stopped.We present a case study with real-life datasets collected at the University of Seville (Spain) and a public dataset from the University of Saskatchewan. The application of our method to these datasets showed that more than 42% of one trace, and almost 20% of another were unnecessarily collected, thereby showing that our proposed method can be an efficient approach for collecting training data at the production environment.This work was supported in part by the Corporación Tecnológica de Andalucía and the University of Seville through the Projects under Grant CTA 1669/22/2017, Grant PI-1786/22/2018, and Grant PI-1736/22/2017

Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations

The IEC 61850 standard has contributed significantly to the substation management and automation process by incorporating the advantages of communications networks into the operation of power substations. However, this modernization process also involves new challenges in other areas. For example, in the field of security, several academic works have shown that the same attacks used in computer networks (DoS, Sniffing, Tampering, Spoffing among others), can also compromise the operation of a substation. This article evaluates the applicability of hierarchical clustering algorithms and statistical type descriptors (averages), in the identification of anomalous patterns of traffic in communication networks for power substations based on the IEC 61850 standard. The results obtained show that, using a hierarchical algorithm with Euclidean distance proximity criterion and simple link grouping method, a correct classification is achieved in the following operation scenarios: 1) Normal traffic, 2) IED disconnection, 3) Network discovery attack, 4) DoS attack, 5) IED spoofing attack and 6) Failure on the high voltage line. In addition, the descriptors used for the classification proved equally effective with other unsupervised clustering techniques such as K-means (partitional-type clustering), or LAMDA (diffuse-type clustering)

Network traffic classification : from theory to practice

Since its inception until today, the Internet has been in constant transformation. The analysis and monitoring of data networks try to shed some light on this huge black box of interconnected computers. In particular, the classification of the network traffic has become crucial for understanding the Internet. During the last years, the research community has proposed many solutions to accurately identify and classify the network traffic. However, the continuous evolution of Internet applications and their techniques to avoid detection make their identification a very challenging task, which is far from being completely solved. This thesis addresses the network traffic classification problem from a more practical point of view, filling the gap between the real-world requirements from the network industry, and the research carried out. The first block of this thesis aims to facilitate the deployment of existing techniques in production networks. To achieve this goal, we study the viability of using NetFlow as input in our classification technique, a monitoring protocol already implemented in most routers. Since the application of packet sampling has become almost mandatory in large networks, we also study its impact on the classification and propose a method to improve the accuracy in this scenario. Our results show that it is possible to achieve high accuracy with both sampled and unsampled NetFlow data, despite the limited information provided by NetFlow. Once the classification solution is deployed it is important to maintain its accuracy over time. Current network traffic classification techniques have to be regularly updated to adapt them to traffic changes. The second block of this thesis focuses on this issue with the goal of automatically maintaining the classification solution without human intervention. Using the knowledge of the first block, we propose a classification solution that combines several techniques only using Sampled NetFlow as input for the classification. Then, we show that classification models suffer from temporal and spatial obsolescence and, therefore, we design an autonomic retraining system that is able to automatically update the models and keep the classifier accurate along time. Going one step further, we introduce next the use of stream-based Machine Learning techniques for network traffic classification. In particular, we propose a classification solution based on Hoeffding Adaptive Trees. Apart from the features of stream-based techniques (i.e., process an instance at a time and inspect it only once, with a predefined amount of memory and a bounded amount of time), our technique is able to automatically adapt to the changes in the traffic by using only NetFlow data as input for the classification. The third block of this thesis aims to be a first step towards the impartial validation of state-of-the-art classification techniques. The wide range of techniques, datasets, and ground-truth generators make the comparison of different traffic classifiers a very difficult task. To achieve this goal we evaluate the reliability of different Deep Packet Inspection-based techniques (DPI) commonly used in the literature for ground-truth generation. The results we obtain show that some well-known DPI techniques present several limitations that make them not recommendable as a ground-truth generator in their current state. In addition, we publish some of the datasets used in our evaluations to address the lack of publicly available datasets and make the comparison and validation of existing techniques easier

Information leakage and steganography: detecting and blocking covert channels

This PhD Thesis explores the threat of information theft perpetrated by malicious insiders. As opposite to outsiders, insiders have access to information assets belonging the organization, know the organization infrastructure and more importantly, know the value of the different assets the organization holds. The risk created by malicious insiders have led both the research community and commercial providers to spend efforts on creating mechanisms and solutions to reduce it. However, the lack of certain controls by current proposals may led security administrators to a false sense of security that could actually ease information theft attempts. As a first step of this dissertation, a study of current state of the art proposals regarding information leakage protections has been performed. This study has allowed to identify the main weaknesses of current proposals which are mainly the usage of steganographic algorithms, the lack of control of modern mobile devices and the lack of control of the action the insiders perform inside the different trusted applications they commonly use. Each of these drawbacks have been explored during this dissertation. Regarding the usage of steganographic algorithms, two different steganographic systems have been proposed. First, a steganographic algorithm that transforms source code into innocuous text has been presented. This system uses free context grammars and to parse the source code to be hidden and produce an innocuous text. This system could be used to extract valuable source code from software development environments, where security restrictions are usually softened. Second, a steganographic application for iOS devices has also been presented. This application, called “Hide It In” allows to embed images into other innocuous images and send those images through the device email account. This application includes a cover mode that allows to take pictures without showing that fact in the device screen. The usage of these kinds of applications is suitable in most of the environments which handle sensitive information, as most of them do not incorporate mechanisms to control the usage of advanced mobile devices. The application, which is already available at the Apple App Store, has been downloaded more than 5.000 times. In order to protect organizations against the malicious usage of steganography, several techniques can be implemented. In this thesis two different approaches are presented. First, steganographic detectors could be deployed along the organization to detect possible transmissions of stego-objects outside the organization perimeter. In this regard, a proposal to detect hidden information inside executable files has been presented. The proposed detector, which measures the assembler instruction selection made by compilers, is able to correctly identify stego-objects created through the tool Hydan. Second, steganographic sanitizers could be deployed over the organization infrastructure to reduce the capacity of covert channels that can transmit information outside the organization. In this regard, a framework to avoid the usage of steganography over the HTTP protocol has been proposed. The presented framework, diassembles HTTP messages, overwrites the possible carriers of hidden information with random noise and assembles the HTTP message again. Obtained results show that it is possible to highly reduce the capacity of covert channels created through HTTP. However, the system introduces a considerable delay in communications. Besides steganography, this thesis has also addressed the usage of trusted applications to extract information from organizations. Although applications execution inside an organization can be restricted, trusted applications used to perform daily tasks are generally executed without any restrictions. However, the complexity of such applications can be used by an insider to transform information in such a way that deployed information protection solutions are not able to detect the transformed information as sensitive. In this thesis, a method to encrypt sensitive information using trusted applications is presented. Once the information has been encrypted it is possible to extract it outside the organization without raising any alarm in the deployed security systems. This technique has been successfully evaluated against a state of the art commercial data leakage protection solution. Besides the presented evasion technique, several improvements to enhance the security of current DLP solutions are presented. These are specifically focused in avoiding information leakage through the usage of trusted applications. The contributions of this dissertation have shown that current information leakage protection mechanisms do not fully address all the possible attacks that a malicious insider can commit to steal sensitive information. However, it has been shown that it is possible to implement mechanisms to avoid the extraction of sensitive information by malicious insiders. Obviously, avoiding such attacks does not mean that all possible threats created by malicious insiders are addressed. It is necessary then, to continue studying the threats that malicious insiders pose to the confidentiality of information assets and the possible mechanisms to mitigate them. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Esta tesis doctoral explora la amenaza creada por los empleados maliciosos en lo referente a la confidencialidad de la información sensible (o privilegiada) en posesión de una organización. Al contrario que los atacantes externos a la organización, los atacantes internos poseen de acceso a los activos de información pertenecientes a la organización, conocen la infraestructura de la misma y lo más importante, conocen el valor de los mismos. El riesgo creado por los empleados maliciosos (o en general atacantes internos) ha llevado tanto a la comunidad investigadora como a los proveedores comerciales de seguridad de la información a la creación de mecanismos y soluciones para reducir estas amenazas. Sin embargo, la falta de controles por parte de ciertas propuestas actuales pueden inducir una falsa sensación de seguridad en los administradores de seguridad de las organizaciones, facilitando los posibles intentos de robo de información. Para la realización de esta tesis doctoral, en primer lugar se ha realizado un estudio de las propuestas actuales con respecto a la protección de fugas de información. Este estudio ha permitido identificar las principales debilidades de las mismas, que son principalmente la falta de control sobre el uso de algoritmos esteganográficos, la falta de control de sobre dispositivos móviles avanzados y la falta de control sobre las acciones que realizan los empleados en el interior de las organizaciones. Cada uno de los problemas identificados ha sido explorado durante la realización de esta tesis doctoral. En lo que respecta al uso de algoritmos esteganográficos, esta tesis incluye la propuesta de dos sistemas de ocultación de información. En primer lugar, se presenta un algoritmo esteganográfico que transforma código fuente en texto inocuo. Este sistema utiliza gramáticas libres de contexto para transformar el código fuente a ocultar en un texto inocuo. Este sistema podría ser utilizado para extraer código fuente valioso de entornos donde se realiza desarrollo de software (donde las restricciones de seguridad suelen ser menores). En segundo lugar, se propone una aplicación esteganográfica para dispositivos móviles (concretamente iOS). Esta aplicación, llamada “Hide It In” permite incrustar imágenes en otras inocuas y enviar el estegoobjeto resultante a través de la cuenta de correo electrónico del dispositivo. Esta aplicación incluye un modo encubierto, que permite tomar imágenes mostrando en el propio dispositivo elementos del interfaz diferentes a los de a cámara, lo que permite tomar fotografías de forma inadvertida. Este tipo de aplicaciones podrían ser utilizadas por empleados malicios en la mayoría de los entornos que manejan información sensible, ya que estos no suelen incorporar mecanismos para controlar el uso de dispositivos móviles avanzados. La aplicación, que ya está disponible en la App Store de Apple, ha sido descargada más de 5.000 veces. Otro objetivo de la tesis ha sido prevenir el uso malintencionado de técnicas esteganográficas. A este respecto, esta tesis presenta dos enfoques diferentes. En primer lugar, se pueden desplegar diferentes detectores esteganográficos a lo largo de la organización. De esta forma, se podrían detectar las posibles transmisiones de estego-objetos fuera del ámbito de la misma. En este sentido, esta tesis presenta un algoritmo de estegoanálisis para la detección de información oculta en archivos ejecutables. El detector propuesto, que mide la selección de instrucciones realizada por los compiladores, es capaz de identificar correctamente estego-objetos creados a través de la herramienta de Hydan. En segundo lugar, los “sanitizadores” esteganográficos podrían ser desplegados a lo largo de la infraestructura de la organización para reducir la capacidad de los posibles canales encubiertos que pueden ser utilizados para transmitir información sensible de forma descontrolada.. En este sentido, se ha propuesto un marco para evitar el uso de la esteganografía a través del protocolo HTTP. El marco presentado, descompone los mensajes HTTP, sobrescribe los posibles portadores de información oculta mediante la inclusión de ruido aleatorio y reconstruye los mensajes HTTP de nuevo. Los resultados obtenidos muestran que es posible reducir drásticamente la capacidad de los canales encubiertos creados a través de HTTP. Sin embargo, el sistema introduce un retraso considerable en las comunicaciones. Además de la esteganografía, esta tesis ha abordado también el uso de aplicaciones de confianza para extraer información sensible de las organizaciones. Aunque la ejecución de aplicaciones dentro de una organización puede ser restringida, las aplicaciones de confianza, que se utilizan generalmente para realizar tareas cotidianas dentro de la organización, se ejecutan normalmente sin ninguna restricción. Sin embargo, la complejidad de estas aplicaciones puede ser utilizada para transformar la información de tal manera que las soluciones de protección ante fugas de información desplegadas no sean capaces de detectar la información transformada como sensibles. En esta tesis, se presenta un método para cifrar información sensible mediante el uso de aplicaciones de confianza. Una vez que la información ha sido cifrada, es posible extraerla de la organización sin generar alarmas en los sistemas de seguridad implementados. Esta técnica ha sido evaluada con éxito contra de una solución comercial para la prevención de fugas de información. Además de esta técnica de evasión, se han presentado varias mejoras en lo que respecta a la seguridad de las actuales soluciones DLP. Estas, se centran específicamente en evitar la fuga de información a través del uso de aplicaciones de confianza. Las contribuciones de esta tesis han demostrado que los actuales mecanismos para la protección ante fugas de información no responden plenamente a todos los posibles ataques que puedan ejecutar empleados maliciosos. Sin embargo, también se ha demostrado que es posible implementar mecanismos para evitar la extracción de información sensible mediante los mencionados ataques. Obviamente, esto no significa que todas las posibles amenazas creadas por empleados maliciosos hayan sido abordadas. Es necesario por lo tanto, continuar el estudio de las amenazas en lo que respecta a la confidencialidad de los activos de información y los posibles mecanismos para mitigar las mismas

Detection and Explanation of Distributed Denial of Service (DDoS) Attack Through Interpretable Machine Learning

Distributed denial of service (DDoS) is a network-based attack where the aim of the attacker is to overwhelm the victim server. The attacker floods the server by sending enormous amount of network packets in a distributed manner beyond the servers capacity and thus causing the disruption of its normal service. In this dissertation, we focus to build intelligent detectors that can learn by themselves with less human interactions and detect DDoS attacks accurately. Machine learning (ML) has promising outcomes throughout the technologies including cybersecurity and provides us with intelligence when applied on Intrusion Detection Systems (IDSs). In addition, from the state-of-the-art ML-based IDSs, the Ensemble classifier (combination of classifiers) outperforms single classifier. Therefore, we have implemented both supervised and unsupervised ensemble frameworks to build IDSs for better DDoS detection accuracy with lower false alarms compared to the existing ones. Our experimentation, done with the most popular and benchmark datasets such as NSL-KDD, UNSW-NB15, and CICIDS2017, have achieved at most detection accuracy of 99.1% with the lowest false positive rate of 0.01%. As feature selection is one of the mandatory preprocessing phases in ML classification, we have designed several feature selection techniques for better performances in terms of DDoS detection accuracy, false positive alarms, and training times. Initially, we have implemented an ensemble framework for feature selection (FS) methods which combines almost all well-known FS methods and yields better outcomes compared to any single FS method.The goal of my dissertation is not only to detect DDoS attacks precisely but also to demonstrate explanations for these detections. Interpretable machine learning (IML) technique is used to explain a detected DDoS attack with the help of the effectiveness of the corresponding features. We also have implemented a novel feature selection approach based on IML which helps to find optimum features that are used further to retrain our models. The retrained model gives better performances than general feature selection process. Moreover, we have developed an explainer model using IML that identifies detected DDoS attacks with proper explanations based on effectiveness of the features. The contribution of this dissertation is five-folded with the ultimate goal of detecting the most frequent DDoS attacks in cyber security. In order to detect DDoS attacks, we first used ensemble machine learning classification with both supervised and unsupervised classifiers. For better performance, we then implemented and applied two feature selection approaches, such as ensemble feature selection framework and IML based feature selection approach, both individually and in a combination with supervised ensemble framework. Furthermore, we exclusively added explanations for the detected DDoS attacks with the help of explainer models that are built using LIME and SHAP IML methods. To build trustworthy explainer models, a detailed survey has been conducted on interpretable machine learning methods and on their associated tools. We applied the designed framework in various domains, like smart grid and NLP-based IDS to verify its efficacy and ability of performing as a generic model

Detection of vulnerabilities and automatic protection for web applications

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2016In less than three decades of existence, the Web evolved from a platform for accessing hypermedia to a framework for running complex web applications. These applications appear in many forms, from small home-made to large-scale commercial services such as Gmail, Office 365, and Facebook. Although a significant research effort on web application security has been on going for a while, these applications have been a major source of problems and their security continues to be challenged. An important part of the problem derives from vulnerable source code, often written in unsafe languages like PHP, and programmed by people without the appropriate knowledge about secure coding, who leave flaws in the applications. Nowadays the most exploited vulnerability category is the input validation, which is directly related with the user inputs inserted in web application forms. The thesis proposes methodologies and tools for the detection of input validation vulnerabilities in source code and for the protection of web applications written in PHP, using source code static analysis, machine learning and runtime protection techniques. An approach based on source code static analysis is used to identify vulnerabilities in applications programmed with PHP. The user inputs are tracked with taint analysis to determine if they reach a PHP function susceptible to be exploited. Then, machine learning is applied to determine if the identified flaws are actually vulnerabilities. In the affirmative case, the results of static analysis are used to remove the flaws, correcting the source code automatically thus protecting the web application. A new technique for source code static analysis is suggested to automatically learn about vulnerabilities and then to detect them. Machine learning applied to natural language processing is used to, in a first instance, learn characteristics about flaws in the source code, classifying it as being vulnerable or not, and then discovering and identifying the vulnerabilities. A runtime protection technique is also proposed to flag and block injection attacks against databases. The technique is implemented inside the database management system to improve the effectiveness of the detection of attacks, avoiding a semantic mismatch. Source code identifiers are employed so that, when an attack is flagged, the vulnerability is localized in the source code. Overall this work allowed the identification of about 1200 vulnerabilities in open source web applications available in the Internet, 560 of which previously unknown. The unknown vulnerabilities were reported to the corresponding software developers and most of them have already been removed.Em duas décadas de existência, a web evoluiu de uma plataforma para aceder a conteúdos hipermédia para uma infraestrutura para execução de aplicações complexas. Estas aplicações têm várias formas, desde aplicações pequenas e caseiras, a aplicações complexas e de grande escala e para diversos propósitos, como por exemplo serviços comerciais como o Gmail, Office 365 e Facebook. Apesar do grande esforço de investigação da última década em como tornar as aplicações web seguras, estas continuam a ser uma fonte de problemas e a sua segurança um desafio. Uma parte importante deste problema deriva de código fonte vulnerável, muitas vezes desenvolvido com linguagens de programação com poucas validações e construído por pessoas sem os conhecimentos mais adequados para uma programação segura. Atualmente a categoria de vulnerabilidades mais explorada é a de validação de input, diretamente relacionada com os dados (inputs) que os utilizadores inserem nas aplicações web. A tese propõe metodologias para a detecção e remoção de vulnerabilidades no código fonte e para a proteção das aplicações web em tempo de execução, empregando técnicas como a análise estática de código, aprendizagem máquina e protecção em tempo de execução. Numa primeira fase, a análise estática é utilizada para descobrir e identificar vulnerabilidades no código programado na linguagem PHP. Os inputs dos utilizadores são rastreados e é verificado se estes são parâmetros de funções PHP susceptíveis de serem exploradas. A combinação desta técnica com a aprendizagem máquina aplicada em minerização de dados é proposta para prever se as vulnerabilidades detectadas são falsos positivos ou reais. Caso sejam reais, o resultado da análise estática de código é utilizado para eliminá-las, corrigindo o código fonte automaticamente com fixes (remendos) e protegendo assim as aplicações web. A tese apresenta também uma nova técnica de análise estática de código para descobrir vulnerabilidades. A técnica aprende o que é código vulnerável e depois tira partido desse conhecimento para localizar problemas. A aprendizagem máquina aplicada ao processamento de linguagem natural é utilizada para, numa primeira instância, aprender aspectos que caracterizam as vulnerabilidades, para depois processar e analisar o código fonte, classificando-o como sendo ou não vulnerável, descobrindo e identificando os erros. Numa terceira fase, é proposta uma nova técnica de proteção em tempo de execução para descobrir e bloquear ataques de injeção contra bases de dados. A técnica é concretizada dentro do sistema de gestão de bases de dados para melhorar a eficácia na detecção dos ataques. É utilizada conjuntamente com identificadores de código fonte que, quando um ataque é sinalizado, permitem identificar a vulnerabilidade no programa. No total este trabalho permitiu a identificação de cerca de 1200 vulnerabilidades em aplicações web de código aberto disponíveis na Internet, das quais 560 eram até então desconhecidas. As vulnerabilidades desconhecidas foram reportadas aos autores do software onde foram encontradas e muitas delas já foram removidas

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Building and evaluating privacy-preserving data processing systems

Large-scale data processing prompts a number of important challenges, including guaranteeing that collected or published data is not misused, preventing disclosure of sensitive information, and deploying privacy protection frameworks that support usable and scalable services. In this dissertation, we study and build systems geared for privacy-friendly data processing, enabling computational scenarios and applications where potentially sensitive data can be used to extract useful knowledge, and which would otherwise be impossible without such strong privacy guarantees. For instance, we show how to privately and efficiently aggregate data from many sources and large streams, and how to use the aggregates to extract useful statistics and train simple machine learning models. We also present a novel technique for privately releasing generative machine learning models and entire high-dimensional datasets produced by these models. Finally, we demonstrate that the data used by participants in training generative and collaborative learning models may be vulnerable to inference attacks and discuss possible mitigation strategies