Flexible and Robust Privacy-Preserving Implicit Authentication

Implicit authentication consists of a server authenticating a user based on the user's usage profile, instead of/in addition to relying on something the user explicitly knows (passwords, private keys, etc.). While implicit authentication makes identity theft by third parties more difficult, it requires the server to learn and store the user's usage profile. Recently, the first privacy-preserving implicit authentication system was presented, in which the server does not learn the user's profile. It uses an ad hoc two-party computation protocol to compare the user's fresh sampled features against an encrypted stored user's profile. The protocol requires storing the usage profile and comparing against it using two different cryptosystems, one of them order-preserving; furthermore, features must be numerical. We present here a simpler protocol based on set intersection that has the advantages of: i) requiring only one cryptosystem; ii) not leaking the relative order of fresh feature samples; iii) being able to deal with any type of features (numerical or non-numerical). Keywords: Privacy-preserving implicit authentication, privacy-preserving set intersection, implicit authentication, active authentication, transparent authentication, risk mitigation, data brokers.Comment: IFIP SEC 2015-Intl. Information Security and Privacy Conference, May 26-28, 2015, IFIP AICT, Springer, to appea

EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.Comment: A preliminary version of this paper was published in the Proceedings of the 7th ESORICS International Workshop on Digital Privacy Management (DPM 2012). This is the full version, appearing in the Journal of Computer Securit

PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning

Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs additional security and privacy challenges from intermediate model aggregations and remains the single point of failure issue. To tackle these challenges, we propose a blockchain-based HFL (BHFL) system that operates a permissioned blockchain among edge servers for model aggregation without the need for a centralized cloud server. The employment of blockchain, however, introduces additional overhead. To enable a compact and efficient workflow, we design a novel lightweight consensus algorithm, named Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for local model training. Specifically, the leader node is selected by evaluating the intermediate FEL models from all edge servers instead of other energy-wasting but meaningless calculations. This design thus improves the system efficiency compared with traditional BHFL frameworks. To prevent model plagiarism and bribery voting during the consensus process, we propose Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to motivate continuous contributions from clients to the learning task. Experimental results demonstrate that our proposed BHFL system with the corresponding consensus protocol and incentive mechanism achieves effectiveness, low computational cost, and fairness

Building a privacy-preserving semantic overlay for Peer-to-Peer networks

Searching a Peer-to-Peer (P2P) network without using a central index has been widely investigated but proved to be very difficult. Various strategies have been proposed, however no practical solution to date also addresses privacy concerns. By clustering peers which have similar interests, a semantic overlay provides a method for achieving scalable search. Traditionally, in order to find similar peers, a peer is required to fully expose its preferences for items or content, therefore disclosing this private information. However, in a hostile environment, such as a P2P system, a peer can not know the true identity or intentions of fellow peers. In this paper, we propose two protocols for building a semantic overlay in a privacy-preserving manner by modifying existing solutions to the Private Set Intersection (PSI) problem. Peers in our overlay compute their similarity to other peers in the encrypted domain, allowing them to find similar peers. Using homomorphic encryption, peers can carrying out computations on encrypted values, without needing to decrypt them first. We propose two protocols, one based on the inner product of vectors, the other on multivariate polynomial evaluation, which are able to compute a similarity value between two peers. Both protocols are implemented on top of an existing P2P platform and are designed for actual deployment. Using a supercomputer and a dataset extracted from a real world instance of a semantic overlay, we emulate our protocols in a network consisting of a thousand peers. Finally, we show the actual computational and bandwidth usage of the protocols as recorded during those experiments

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