The Pareto Frontier for Random Mechanisms

We study the trade-offs between strategyproofness and other desiderata, such as efficiency or fairness, that often arise in the design of random ordinal mechanisms. We use approximate strategyproofness to define manipulability, a measure to quantify the incentive properties of non-strategyproof mechanisms, and we introduce the deficit, a measure to quantify the performance of mechanisms with respect to another desideratum. When this desideratum is incompatible with strategyproofness, mechanisms that trade off manipulability and deficit optimally form the Pareto frontier. Our main contribution is a structural characterization of this Pareto frontier, and we present algorithms that exploit this structure to compute it. To illustrate its shape, we apply our results for two different desiderata, namely Plurality and Veto scoring, in settings with 3 alternatives and up to 18 agents.Comment: Working Pape

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Scalable and Robust Distributed Algorithms for Privacy-Preserving Applications

We live in an era when political and commercial entities are increasingly engaging in sophisticated cyber attacks to damage, disrupt, or censor information content and to conduct mass surveillance. By compiling various patterns from user data over time, untrusted parties could create an intimate picture of sensitive personal information such as political and religious beliefs, health status, and so forth. In this dissertation, we study scalable and robust distributed algorithms that guarantee user privacy when communicating with other parties to either solely exchange information or participate in multi-party computations. We consider scalability and robustness requirements in three privacy-preserving areas: secure multi-party computation (MPC), anonymous broadcast, and blocking-resistant Tor bridge distribution. We propose decentralized algorithms for MPC that, unlike most previous work, scale well with the number of parties and tolerate malicious faults from a large fraction of the parties. Our algorithms do not require any trusted party and are fully load-balanced. Anonymity is an essential tool for achieving privacy; it enables individuals to communicate with each other without being identified as the sender or the receiver of the information being exchanged. We show that our MPC algorithms can be effectively used to design a scalable anonymous broadcast protocol. We do this by developing a multi-party shuffling protocol that can efficiently anonymize a sequence of messages in the presence of many faulty nodes. Our final approach for preserving user privacy in cyberspace is to improve Tor; the most popular anonymity network in the Internet. A current challenge with Tor is that colluding corrupt users inside a censorship territory can completely block user\u27s access to Tor by obtaining information about a large fraction of Tor bridges; a type of relay nodes used as the Tor\u27s primary mechanism for blocking-resistance. We describe a randomized bridge distribution algorithm, where all honest users are guaranteed to connect to Tor in the presence of an adversary corrupting an unknown number of users. Our simulations suggest that, with minimal resource costs, our algorithm can guarantee Tor access for all honest users after a small (logarithmic) number of rounds

Privacy, Space and Time: a Survey on Privacy-Preserving Continuous Data Publishing

Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis. The manipulation of such data is useful in numerous application domains, e.g., healthcare, intelligent buildings, and traffic monitoring, to name a few. A high percentage of these data carry information of users\u27 activities and other personal details, and thus their manipulation and sharing arise concerns about the privacy of the individuals involved. To enable the secure—from the users\u27 privacy perspective—data sharing, researchers have already proposed various seminal techniques for the protection of users\u27 privacy. However, the continuous fashion in which data are generated nowadays, and the high availability of external sources of information, pose more threats and add extra challenges to the problem. In this survey, we visit the works done on data privacy for continuous data publishing, and report on the proposed solutions, with a special focus on solutions concerning location or geo-referenced data

Cryptography with anonymity in mind

Advances in information technologies gave a rise to powerful ubiquitous com- puting devices, and digital networks have enabled new ways of fast communication, which immediately found tons of applications and resulted in large amounts of data being transmitted. For decades, cryptographic schemes and privacy-preserving protocols have been studied and researched in order to offer end users privacy of their data and implement useful functionalities at the same time, often trading security properties for cryptographic assumptions and efficiency. In this plethora of cryptographic constructions, anonymity properties play a special role, as they are important in many real-life scenarios. However, many useful cryptographic primitives lack anonymity properties or imply prohibitive costs to achieve them. In this thesis, we expand the territory of cryptographic primitives with anonymity in mind. First, we define Anonymous RAM, a generalization of a single- user Oblivious RAM to multiple mistrusted users, and present two constructions thereof with different trade-offs between assumptions and efficiency. Second, we define an encryption scheme that allows to establish chains of ciphertexts anony- mously and verify their integrity. Furthermore, the aggregatable version of the scheme allows to build a Parallel Anonymous RAM, which enhances Anonymous RAM by supporting concurrent users. Third, we show our technique for construct- ing efficient non-interactive zero-knowledge proofs for statements that consist of both algebraic and arithmetic statements. Finally, we show our framework for constructing efficient single secret leader election protocols, which have been recently identified as an important component in proof-of-stake cryptocurrencies.Fortschritte in der Informationstechnik haben leistungsstarke allgegenwärtige Rechner hervorgerufen, während uns digitale Netzwerke neue Wege für die schnelle Kommunikation ermöglicht haben. Durch die Vielzahl von Anwendungen führte dies zur Übertragung von riesigen Datenvolumen. Seit Jahrzehnten wurden bereits verschiedene kryptographische Verfahren und Technologien zum Datenschutz erforscht und analysiert. Das Ziel ist die Privatsphäre der Benutzer zu schützen und gleichzeitig nützliche Funktionalität anzubieten, was oft mit einem Kompromiss zwischen Sicherheitseigenschaften, kryptographischen Annahmen und Effizienz verbunden ist. In einer Fülle von kryptographischen Konstruktionen spielen Anonymitätseigenschaften eine besondere Rolle, da sie in vielen realistischen Szenarien sehr wichtig sind. Allerdings fehlen vielen kryptographischen Primitive Anonymitätseigenschaften oder sie stehen im Zusammenhang mit erheblichen Kosten. In dieser Dissertation erweitern wir den Bereich von kryptographischen Prim- itiven mit einem Fokus auf Anonymität. Erstens definieren wir Anonymous RAM, eine Verallgemeinerung von Einzelbenutzer-Oblivious RAM für mehrere misstraute Benutzer, und stellen dazu zwei Konstruktionen mit verschiedenen Kompromissen zwischen Annahmen und Effizienz vor. Zweitens definieren wir ein Verschlüsselungsverfahren, das es erlaubt anonym eine Verbindung zwischen Geheimtexten herzustellen und deren Integrität zu überprüfen. Darüber hinaus bietet die aggregierbare Variante von diesem Verfahren an, Parallel Anonymous RAM zu bauen. Dieses verbessert Anonymous RAM, indem es mehrere Benutzer in einer parallelen Ausführung unterstützen kann. Drittens zeigen wir eine Meth- ode für das Konstruieren effizienter Zero-Knowledge-Protokolle, die gleichzeitig aus algebraischen und arithmetischen Teilen bestehen. Zuletzt zeigen wir ein Framework für das Konstruieren effizienter Single-Leader-Election-Protokolle, was kürzlich als ein wichtiger Bestandteil in den Proof-of-Stake Kryptowährungen erkannt worden ist

Anonymity-Preserving Public-Key Encryption: A Constructive Approach

Abstract. A receiver-anonymous channel allows a sender to send a message to a receiver without an adversary learning for whom the message is intended. Wireless broadcast channels naturally provide receiver anonymity, as does multi-casting one message to a receiver population containing the intended receiver. While anonymity and confidentiality appear to be orthogonal properties, making anonymous communication confidential is more involved than one might expect, since the ciphertext might reveal which public key has been used to encrypt. To address this problem, public-key cryptosystems with enhanced security properties have been proposed. We investigate constructions as well as limitations for preserving receiver anonymity when using public-key encryption (PKE). We use the constructive cryptography approach by Maurer and Renner and interpret cryptographic schemes as constructions of a certain ideal resource (e.g. a confidential anonymous channel) from given real resources (e.g. a broadcast channel). We define appropriate anonymous communication resources and show that a very natural resource can be constructed by using a PKE scheme which fulfills three properties that appear in cryptographic literature (IND-CCA, key-privacy, weak robustness). We also show that a desirable stronger variant, preventing the adversary from selective “trial-deliveries ” of messages, is unfortunately unachievable by any PKE scheme, no matter how strong. The constructive approach makes the guarantees achieved by applying a cryptographic scheme explicit in the constructed (ideal) resource; this specifies the exact requirements for the applicability of a cryptographic scheme in a given context. It also allows to decide which of the existing security properties of such a cryptographic scheme are adequate for the considered scenario, and which are too weak or too strong. Here, we show that weak robustness is necessary but that so-called strong robustness is unnecessarily strong in that it does not construct a (natural) stronger resource

Comprehensive survey on big data privacy protection

In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals’ sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.Published versio

Lattice-Based proof of a shuffle

In this paper we present the first fully post-quantum proof of a shuffle for RLWE encryption schemes. Shuffles are commonly used to construct mixing networks (mix-nets), a key element to ensure anonymity in many applications such as electronic voting systems. They should preserve anonymity even against an attack using quantum computers in order to guarantee long-term privacy. The proof presented in this paper is built over RLWE commitments which are perfectly binding and computationally hiding under the RLWE assumption, thus achieving security in a post-quantum scenario. Furthermore we provide a new definition for a secure mixing node (mix-node) and prove that our construction satisfies this definition.Peer ReviewedPostprint (author's final draft