When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance

We show that it is possible to achieve information theoretic location privacy for secondary users (SUs) in database-driven cognitive radio networks (CRNs) with an end-to-end delay less than a second, which is significantly better than that of the existing alternatives offering only a computational privacy. This is achieved based on a keen observation that, by the requirement of Federal Communications Commission (FCC), all certified spectrum databases synchronize their records. Hence, the same copy of spectrum database is available through multiple (distinct) providers. We harness the synergy between multi-server private information retrieval (PIR) and database- driven CRN architecture to offer an optimal level of privacy with high efficiency by exploiting this observation. We demonstrated, analytically and experimentally with deployments on actual cloud systems that, our adaptations of multi-server PIR outperform that of the (currently) fastest single-server PIR by a magnitude of times with information theoretic security, collusion resiliency, and fault-tolerance features. Our analysis indicates that multi-server PIR is an ideal cryptographic tool to provide location privacy in database-driven CRNs, in which the requirement of replicated databases is a natural part of the system architecture, and therefore SUs can enjoy all advantages of multi-server PIR without any additional architectural and deployment costs.Comment: 10 pages, double colum

Contributions to privacy in web search engines

Els motors de cerca d’Internet recullen i emmagatzemen informació sobre els seus usuaris per tal d’oferir-los millors serveis. A canvi de rebre un servei personalitzat, els usuaris perden el control de les seves pròpies dades. Els registres de cerca poden revelar informació sensible de l’usuari, o fins i tot revelar la seva identitat. En aquesta tesis tractem com limitar aquests problemes de privadesa mentre mantenim suficient informació a les dades. La primera part d’aquesta tesis tracta els mètodes per prevenir la recollida d’informació per part dels motores de cerca. Ja que aquesta informació es requerida per oferir un servei precís, l’objectiu es proporcionar registres de cerca que siguin adequats per proporcionar personalització. Amb aquesta finalitat, proposem un protocol que empra una xarxa social per tal d’ofuscar els perfils dels usuaris. La segona part tracta la disseminació de registres de cerca. Proposem tècniques que la permeten, proporcionant k-anonimat i minimitzant la pèrdua d’informació.Web Search Engines collects and stores information about their users in order to tailor their services better to their users' needs. Nevertheless, while receiving a personalized attention, the users lose the control over their own data. Search logs can disclose sensitive information and the identities of the users, creating risks of privacy breaches. In this thesis we discuss the problem of limiting the disclosure risks while minimizing the information loss. The first part of this thesis focuses on the methods to prevent the gathering of information by WSEs. Since search logs are needed in order to receive an accurate service, the aim is to provide logs that are still suitable to provide personalization. We propose a protocol which uses a social network to obfuscate users' profiles. The second part deals with the dissemination of search logs. We propose microaggregation techniques which allow the publication of search logs, providing $k$-anonymity while minimizing the information loss

Contributions to Lifelogging Protection In Streaming Environments

Tots els dies, més de cinc mil milions de persones generen algun tipus de dada a través d'Internet. Per accedir a aquesta informació, necessitem utilitzar serveis de recerca, ja siguin motors de cerca web o assistents personals. A cada interacció amb ells, el nostre registre d'accions, logs, s'utilitza per oferir una millor experiència. Per a les empreses, també són molt valuosos, ja que ofereixen una forma de monetitzar el servei. La monetització s'aconsegueix venent dades a tercers, però, els logs de consultes podrien exposar informació confidencial de l'usuari (identificadors, malalties, tendències sexuals, creences religioses) o usar-se per al que es diu "life-logging ": Un registre continu de les activitats diàries. La normativa obliga a protegir aquesta informació. S'han proposat prèviament sistemes de protecció per a conjunts de dades tancats, la majoria d'ells treballant amb arxius atòmics o dades estructurades. Desafortunadament, aquests sistemes no s'adapten quan es fan servir en el creixent entorn de dades no estructurades en temps real que representen els serveis d'Internet. Aquesta tesi té com objectiu dissenyar tècniques per protegir la informació confidencial de l'usuari en un entorn no estructurat d’streaming en temps real, garantint un equilibri entre la utilitat i la protecció de dades. S'han fet tres propostes per a una protecció eficaç dels logs. La primera és un nou mètode per anonimitzar logs de consultes, basat en k-anonimat probabilística i algunes eines de desanonimització per determinar fuites de dades. El segon mètode, s'ha millorat afegint un equilibri configurable entre privacitat i usabilitat, aconseguint una gran millora en termes d'utilitat de dades. La contribució final es refereix als assistents personals basats en Internet. La informació generada per aquests dispositius es pot considerar "life-logging" i pot augmentar els riscos de privacitat de l'usuari. Es proposa un esquema de protecció que combina anonimat de logs i signatures sanitizables.Todos los días, más de cinco mil millones de personas generan algún tipo de dato a través de Internet. Para acceder a esa información, necesitamos servicios de búsqueda, ya sean motores de búsqueda web o asistentes personales. En cada interacción con ellos, nuestro registro de acciones, logs, se utiliza para ofrecer una experiencia más útil. Para las empresas, también son muy valiosos, ya que ofrecen una forma de monetizar el servicio, vendiendo datos a terceros. Sin embargo, los logs podrían exponer información confidencial del usuario (identificadores, enfermedades, tendencias sexuales, creencias religiosas) o usarse para lo que se llama "life-logging": Un registro continuo de las actividades diarias. La normativa obliga a proteger esta información. Se han propuesto previamente sistemas de protección para conjuntos de datos cerrados, la mayoría de ellos trabajando con archivos atómicos o datos estructurados. Desafortunadamente, esos sistemas no se adaptan cuando se usan en el entorno de datos no estructurados en tiempo real que representan los servicios de Internet. Esta tesis tiene como objetivo diseñar técnicas para proteger la información confidencial del usuario en un entorno no estructurado de streaming en tiempo real, garantizando un equilibrio entre utilidad y protección de datos. Se han hecho tres propuestas para una protección eficaz de los logs. La primera es un nuevo método para anonimizar logs de consultas, basado en k-anonimato probabilístico y algunas herramientas de desanonimización para determinar fugas de datos. El segundo método, se ha mejorado añadiendo un equilibrio configurable entre privacidad y usabilidad, logrando una gran mejora en términos de utilidad de datos. La contribución final se refiere a los asistentes personales basados en Internet. La información generada por estos dispositivos se puede considerar “life-logging” y puede aumentar los riesgos de privacidad del usuario. Se propone un esquema de protección que combina anonimato de logs y firmas sanitizables.Every day, more than five billion people generate some kind of data over the Internet. As a tool for accessing that information, we need to use search services, either in the form of Web Search Engines or through Personal Assistants. On each interaction with them, our record of actions via logs, is used to offer a more useful experience. For companies, logs are also very valuable since they offer a way to monetize the service. Monetization is achieved by selling data to third parties, however query logs could potentially expose sensitive user information: identifiers, sensitive data from users (such as diseases, sexual tendencies, religious beliefs) or be used for what is called ”life-logging”: a continuous record of one’s daily activities. Current regulations oblige companies to protect this personal information. Protection systems for closed data sets have previously been proposed, most of them working with atomic files or structured data. Unfortunately, those systems do not fit when used in the growing real-time unstructured data environment posed by Internet services. This thesis aims to design techniques to protect the user’s sensitive information in a non-structured real-time streaming environment, guaranteeing a trade-off between data utility and protection. In this regard, three proposals have been made in efficient log protection. The first is a new method to anonymize query logs, based on probabilistic k-anonymity and some de-anonymization tools to determine possible data leaks. A second method has been improved in terms of a configurable trade-off between privacy and usability, achieving a great improvement in terms of data utility. Our final contribution concerns Internet-based Personal Assistants. The information generated by these devices is likely to be considered life-logging, and it can increase the user’s privacy risks. The proposal is a protection scheme that combines log anonymization and sanitizable signatures

Lightweight Techniques for Private Heavy Hitters

This paper presents a new protocol for solving the private heavy-hitters problem. In this problem, there are many clients and a small set of data-collection servers. Each client holds a private bitstring. The servers want to recover the set of all popular strings, without learning anything else about any client's string. A web-browser vendor, for instance, can use our protocol to figure out which homepages are popular, without learning any user's homepage. We also consider the simpler private subset-histogram problem, in which the servers want to count how many clients hold strings in a particular set without revealing this set to the clients. Our protocols use two data-collection servers and, in a protocol run, each client send sends only a single message to the servers. Our protocols protect client privacy against arbitrary misbehavior by one of the servers and our approach requires no public-key cryptography (except for secure channels), nor general-purpose multiparty computation. Instead, we rely on incremental distributed point functions, a new cryptographic tool that allows a client to succinctly secret-share the labels on the nodes of an exponentially large binary tree, provided that the tree has a single non-zero path. Along the way, we develop new general tools for providing malicious security in applications of distributed point functions. In an experimental evaluation with two servers on opposite sides of the U.S., the servers can find the 200 most popular strings among a set of 400,000 client-held 256-bit strings in 54 minutes. Our protocols are highly parallelizable. We estimate that with 20 physical machines per logical server, our protocols could compute heavy hitters over ten million clients in just over one hour of computation.Comment: To appear in IEEE Security & Privacy 202

Secure Computation Protocols for Privacy-Preserving Machine Learning

Machine Learning (ML) profitiert erheblich von der Verfügbarkeit großer Mengen an Trainingsdaten, sowohl im Bezug auf die Anzahl an Datenpunkten, als auch auf die Anzahl an Features pro Datenpunkt. Es ist allerdings oft weder möglich, noch gewollt, mehr Daten unter zentraler Kontrolle zu aggregieren. Multi-Party-Computation (MPC)-Protokolle stellen eine Lösung dieses Dilemmas in Aussicht, indem sie es mehreren Parteien erlauben, ML-Modelle auf der Gesamtheit ihrer Daten zu trainieren, ohne die Eingabedaten preiszugeben. Generische MPC-Ansätze bringen allerdings erheblichen Mehraufwand in der Kommunikations- und Laufzeitkomplexität mit sich, wodurch sie sich nur beschränkt für den Einsatz in der Praxis eignen. Das Ziel dieser Arbeit ist es, Privatsphäreerhaltendes Machine Learning mittels MPC praxistauglich zu machen. Zuerst fokussieren wir uns auf zwei Anwendungen, lineare Regression und Klassifikation von Dokumenten. Hier zeigen wir, dass sich der Kommunikations- und Rechenaufwand erheblich reduzieren lässt, indem die aufwändigsten Teile der Berechnung durch Sub-Protokolle ersetzt werden, welche auf die Zusammensetzung der Parteien, die Verteilung der Daten, und die Zahlendarstellung zugeschnitten sind. Insbesondere das Ausnutzen dünnbesetzter Datenrepräsentationen kann die Effizienz der Protokolle deutlich verbessern. Diese Beobachtung verallgemeinern wir anschließend durch die Entwicklung einer Datenstruktur für solch dünnbesetzte Daten, sowie dazugehöriger Zugriffsprotokolle. Aufbauend auf dieser Datenstruktur implementieren wir verschiedene Operationen der Linearen Algebra, welche in einer Vielzahl von Anwendungen genutzt werden. Insgesamt zeigt die vorliegende Arbeit, dass MPC ein vielversprechendes Werkzeug auf dem Weg zu Privatsphäre-erhaltendem Machine Learning ist, und die von uns entwickelten Protokolle stellen einen wesentlichen Schritt in diese Richtung dar.Machine learning (ML) greatly benefits from the availability of large amounts of training data, both in terms of the number of samples, and the number of features per sample. However, aggregating more data under centralized control is not always possible, nor desirable, due to security and privacy concerns, regulation, or competition. Secure multi-party computation (MPC) protocols promise a solution to this dilemma, allowing multiple parties to train ML models on their joint datasets while provably preserving the confidentiality of the inputs. However, generic approaches to MPC result in large computation and communication overheads, which limits the applicability in practice. The goal of this thesis is to make privacy-preserving machine learning with secure computation practical. First, we focus on two high-level applications, linear regression and document classification. We show that communication and computation overhead can be greatly reduced by identifying the costliest parts of the computation, and replacing them with sub-protocols that are tailored to the number and arrangement of parties, the data distribution, and the number representation used. One of our main findings is that exploiting sparsity in the data representation enables considerable efficiency improvements. We go on to generalize this observation, and implement a low-level data structure for sparse data, with corresponding secure access protocols. On top of this data structure, we develop several linear algebra algorithms that can be used in a wide range of applications. Finally, we turn to improving a cryptographic primitive named vector-OLE, for which we propose a novel protocol that helps speed up a wide range of secure computation tasks, within private machine learning and beyond. Overall, our work shows that MPC indeed offers a promising avenue towards practical privacy-preserving machine learning, and the protocols we developed constitute a substantial step in that direction