Privacy-preserving distributed data mining

This thesis is concerned with privacy-preserving distributed data mining algorithms. The main challenges in this setting are inference attacks and the formation of collusion groups. The inference problem is the reconstruction of sensitive data by attackers from non-sensitive sources, such as intermediate results, exchanged messages, or public information. Moreover, in a distributed scenario, malicious insiders can organize collusion groups to deploy more effective inference attacks. This thesis shows that existing privacy measures do not adequately protect privacy against inference and collusion. Therefore, in this thesis, new measures based on information theory are developed to overcome the identiffied limitations. Furthermore, a new distributed data clustering algorithm is presented. The clustering approach is based on a kernel density estimates approximation that generates a controlled amount of ambiguity in the density estimates and provides privacy to original data. Besides, this thesis also introduces the first privacy-preserving algorithms for frequent pattern discovery in a distributed time series. Time series are transformed into a set of n-dimensional data points and finding frequent patterns reduced to finding local maxima in the n-dimensional density space. The proposed algorithms are linear in the size of the dataset with low communication costs, validated by experimental evaluation using different datasets.Diese Arbeit befasst sich mit vertraulichkeitsbewahrendem Data Mining in verteilten Umgebungen mit Schwerpunkt auf ausgewählten N-Agenten-Angriffsszenarien für das Inferenzproblem im Data-Clustering und der Zeitreihenanalyse. Dabei handelt es sich um Angriffe von einzelnen oder Teilgruppen von Agenten innerhalb einer verteilten Data Mining-Gruppe oder von einem einzelnen Agenten außerhalb dieser Gruppe. Zunächst werden in dieser Arbeit zwei neue Privacy-Maße vorgestellt, die im Gegensatz zu bislang existierenden, die im verteilten Data Mining allgemein geforderte Eigenschaften zur Vertraulichkeitsbewahrung erfüllen und bei denen sich der gemessene Grad der Vertraulichkeit auf die verwendete Datenanalysemethode und die Anzahl von Angreifern bezieht. Für den Zweck eines vertraulichkeitsbewahrenden, verteilten Data-Clustering wird ein neues Kernel-Dichteabschätzungsbasiertes Verfahren namens KDECS vorgestellt. KDECS verwendet eine Approximation der originalen, lokalen Kernel-Dichteschätzung, so dass die ursprünglichen Daten anderer Agenten in der Data Mining-Gruppe mit einer höheren Wahrscheinlichkeit als einem hierfür vorgegebenen Wert nicht mehr zu rekonstruieren sind. Das Verfahren ist nachweislich sicherer als Data-Clustering mit generativen Mixture Modellen und SMC-basiert sicherem k-means Data-Clustering. Zusätzlich stellen wir neue Verfahren, namens DPD-TS, DPD-HE und DPDFS, für eine vertraulichkeitsbewahrende, verteilte Mustererkennung in Zeitreihen vor, deren Komplexität und Sicherheitsgrad wir mit den zuvor erwähnten neuen Privacy-Maßen analysieren. Dabei hängt ein von einzelnen Agenten einer Data Mining-Gruppe jeweils vorgegebener, minimaler Sicherheitsgrad von DPD-TS und DPD-FS nur von der Dimensionsreduktion der Zeitreihenwerte und ihrer Diskretisierung ab und kann leicht überprüft werden. Einen noch besseren Schutz von sensiblen Daten bietet das Verfahren DPD HE mit Hilfe von homomorpher Verschlüsselung. Neben der theoretischen Analyse wurden die experimentellen Leistungsbewertungen der entwickelten Verfahren mit verschiedenen, öffentlich verfügbaren Datensätzen durchgeführt

Blockchain leveraged decentralized IoT eHealth framework

Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

An Evaluation of the Security of the Bitcoin Peer-To-Peer Network

Bitcoin is a decentralised digital currency that relies on cryptography rather than trusted third parties such as central banks for its security. Underpinning the operation of the currency is a peer-to-peer (P2P) network that facilitates the execution of transactions by end users, as well as the transaction confirmation process known as bitcoin mining. The security of this P2P network is vital for the currency to function and subversion of the underlying network can lead to attacks on bitcoin users including theft of bitcoins, manipulation of the mining process and denial of service (DoS). As part of this paper the network protocol and bitcoin core software are analysed, with three bitcoin message exchanges (the connection handshake, GETHEADERS/HEADERS and MEMPOOL/INV) found to be potentially vulnerable to spoofing and use in distributed denial of service (DDoS) attacks. Possible solutions to the identified weaknesses and vulnerabilities are evaluated, such as the introduction of random nonces into network messages exchanges.Comment: 8 Pages, 7 Figures, Conferenc