A Balanced Trust-Based Method to Counter Sybil and Spartacus Attacks in Chord

A Sybil attack is one of the main challenges to be addressed when securing peer-to-peer networks, especially those based on Distributed Hash Tables (DHTs). Tampering routing tables by means of multiple fake identities can make routing, storing, and retrieving operations significantly more difficult and time-consuming. Countermeasures based on trust and reputation have already proven to be effective in some contexts, but one variant of the Sybil attack, the Spartacus attack, is emerging as a new threat and its effects are even riskier and more difficult to stymie. In this paper, we first improve a well-known and deployed DHT (Chord) through a solution mixing trust with standard operations, for facing a Sybil attack affecting either routing or storage and retrieval operations. This is done by maintaining the least possible overhead for peers. Moreover, we extend the solution we propose in order for it to be resilient also against a Spartacus attack, both for an iterative and for a recursive lookup procedure. Finally, we validate our findings by showing that the proposed techniques outperform other trust-based solutions already known in the literature as well

Proof of Latency Using a Verifiable Delay Function

In this thesis I present an interactive public-coin protocol called Proof of Latency (PoL) that aims to improve connections in peer-to-peer networks by measuring latencies with logical clocks built from verifiable delay functions (VDF). PoL is a tuple of three algorithms, Setup(e, λ), VCOpen(c, e), and Measure(g, T, l_p, l_v). Setup creates a vector commitment (VC), from which a vector commitment opening corresponding to a collaborator's public key is taken in VCOpen, which then gets used to create a common reference string used in Measure. If no collusion gets detected by neither party, a signed proof is ready for advertising. PoL is agnostic in terms of the individual implementations of the VC or VDF used. This said, I present a proof of concept in the form of a state machine implemented in Rust that uses RSA-2048, Catalano-Fiore vector commitments and Wesolowski's VDF to demonstrate PoL. As VDFs themselves have been shown to be useful in timestamping, they seem to work as a measurement of time in this context as well, albeit requiring a public performance metric for each peer to compare to during the measurement. I have imagined many use cases for PoL, like proving a geographical location, working as a benchmark query, or using the proofs to calculate VDFs with the latencies between peers themselves. As it stands, PoL works as a distance bounding protocol between two participants, considering their computing performance is relatively similar. More work is needed to verify the soundness of PoL as a publicly verifiable proof that a third party can believe in.Tässä tutkielmassa esitän interaktiivisen protokollan nimeltä Proof of latency (PoL), joka pyrkii parantamaan yhteyksiä vertaisverkoissa mittaamalla viivettä todennettavasta viivefunktiosta rakennetulla loogisella kellolla. Proof of latency koostuu kolmesta algoritmista, Setup(e, λ), VCOpen(c, e) ja Measure(g, T, l_p, l_v). Setup luo vektorisitoumuksen, josta luodaan avaus algoritmissa VCOpen avaamalla vektorisitoumus indeksistä, joka kuvautuu toisen mittaavan osapuolen julkiseen avaimeen. Tätä avausta käytetään luomaan yleinen viitemerkkijono, jota käytetään algoritmissa Measure alkupisteenä molempien osapuolien todennettavissa viivefunktioissa mittaamaan viivettä. Jos kumpikin osapuoli ei huomaa virheitä mittauksessa, on heidän allekirjoittama todistus valmis mainostettavaksi vertaisverkossa. PoL ei ota kantaa sen käyttämien kryptografisten funktioiden implementaatioon. Tästä huolimatta olen ohjelmoinut protokollasta prototyypin Rust-ohjelmointikielellä käyttäen RSA-2048:tta, Catalano-Fiore--vektorisitoumuksia ja Wesolowskin todennettavaa viivefunktiota protokollan esittelyyn. Todistettavat viivefunktiot ovat osoittaneet hyödyllisiksi aikaleimauksessa, mikä näyttäisi osoittavan niiden soveltumisen myös ajan mittaamiseen tässä konteksissa, huolimatta siitä että jokaisen osapuolen tulee ilmoittaa julkisesti teholukema, joka kuvaa niiden tehokkuutta viivefunktioiden laskemisessa. Toinen osapuoli käyttää tätä lukemaa arvioimaan valehteliko toinen viivemittauksessa. Olen kuvitellut monta käyttökohdetta PoL:lle, kuten maantieteellisen sijainnin todistaminen, suorituskykytestaus, tai itse viivetodistuksien käyttäminen uusien viivetodistusten laskemisessa vertaisverkon osallistujien välillä. Tällä hetkellä PoL toimii etäisyydenmittausprotokollana kahden osallistujan välillä, jos niiden suorituskyvyt ovat tarpeeksi lähellä toisiaan. Protokolla tarvitsee lisätutkimusta sen suhteen, voiko se toimia uskottavana todistuksena kolmansille osapuolille kahden vertaisverkon osallistujan välisestä viiveestä

Randomness, Age, Work: Ingredients for Secure Distributed Hash Tables

Distributed Hash Tables (DHTs) are a popular and natural choice when dealing with dynamic resource location and routing. DHTs basically provide two main functions: saving (key, value) records in a network environment and, given a key, find the node responsible for it, optionally retrieving the associated value. However, all predominant DHT designs suffer a number of security flaws that expose nodes and stored data to a number of malicious attacks, ranging from disrupting correct DHT routing to corrupting data or making it unavailable. Thus even if DHTs are a standard layer for some mainstream systems (like BitTorrent or KAD clients), said vulnerabilities may prevent more security-aware systems from taking advantage of the ease of indexing and publishing on DHTs. Through the years a variety of solutions to the security flaws of DHTs have been proposed both from academia and practitioners, ranging from authentication via Central Authorities to social-network based ones. These solutions are often tailored to DHT specific implementations, simply try to mitigate without eliminating hostile actions aimed at resources or nodes. Moreover all these solutions often sports serious limitations or make strong assumptions on the underlying network. We present, after after providing a useful abstract model of the DHT protocol and infrastructure, two new primitives. We extend a “standard” proof-of-work primitive making of it also a “proof of age” primitive (informally, allowing a node to prove it is “sufficiently old”) and a “shared random seed” primitive (informally, producing a new, shared, seed that was completely unpredictable in a “sufficiently remote” past). These primitives are then integrated into the basic DHT model obtaining an “enhanced” DHT design, resilient to many common attacks. This work also shows how to adapt a Block Chain scheme – a continuously growing list of records (or blocks) protected from alteration or forgery – to provide a possible infrastructure for our proposed secure design. Finally a working proof-of-concept software implementing an “enhanced” Kademlia-based DHT is presented, together with some experimental results showing that, in practice, the performance overhead of the additional security layer is more than tolerable. Therefore this work provides a threefold contribution. It describes a general set of new primitives (adaptable to any DHT matching our basic model) achieving a secure DHT; it proposes an actionable design to attain said primitives; it makes public a proof-of-concept implementation of a full “enhanced” DHT system, which a preliminary performance evaluation shows to be actually usable in practice

Supporting lay users in privacy decisions when sharing sensitive data

The first part of the thesis focuses on assisting users in choosing their privacy settings, by using machine learning to derive the optimal set of privacy settings for the user. In contrast to other work, our approach uses context factors as well as individual factors to provide a personalized set of privacy settings. The second part consists of a set of intelligent user interfaces to assist the users throughout the complete privacy journey, from defining friend groups that allow targeted information sharing; through user interfaces for selecting information recipients, to find possible errors or unusual settings, and to refine them; up to mechanisms to gather in-situ feedback on privacy incidents, and investigating how to use these to improve a user’s privacy in the future. Our studies have shown that including tailoring the privacy settings significantly increases the correctness of the predicted privacy settings; whereas the user interfaces have been shown to significantly decrease the amount of unwanted disclosures.Insbesondere nach den jüngsten Datenschutzskandalen in sozialen Netzwerken wird der Datenschutz für Benutzer immer wichtiger. Obwohl die meisten Benutzer behaupten Wert auf Datenschutz zu legen, verhalten sie sich online allerdings völlig anders: Sie lassen die meisten Datenschutzeinstellungen der online genutzten Dienste, wie z. B. von sozialen Netzwerken oder Diensten zur Standortfreigabe, unberührt und passen sie nicht an ihre Datenschutzanforderungen an. In dieser Arbeit werde ich einen Ansatz zur Lösung dieses Problems vorstellen, der auf zwei verschiedenen Säulen basiert. Der erste Teil konzentriert sich darauf, Benutzer bei der Auswahl ihrer Datenschutzeinstellungen zu unterstützen, indem maschinelles Lernen verwendet wird, um die optimalen Datenschutzeinstellungen für den Benutzer abzuleiten. Im Gegensatz zu anderen Arbeiten verwendet unser Ansatz Kontextfaktoren sowie individuelle Faktoren, um personalisierte Datenschutzeinstellungen zu generieren. Der zweite Teil besteht aus einer Reihe intelligenter Benutzeroberflächen, die die Benutzer in verschiedene Datenschutzszenarien unterstützen. Dies beginnt bei einer Oberfläche zur Definition von Freundesgruppen, die im Anschluss genutzt werden können um einen gezielten Informationsaustausch zu ermöglichen, bspw. in sozialen Netzwerken; über Benutzeroberflächen um die Empfänger von privaten Daten auszuwählen oder mögliche Fehler oder ungewöhnliche Datenschutzeinstellungen zu finden und zu verfeinern; bis hin zu Mechanismen, um In-Situ- Feedback zu Datenschutzverletzungen zum Zeitpunkt ihrer Entstehung zu sammeln und zu untersuchen, wie diese verwendet werden können, um die Privatsphäreeinstellungen eines Benutzers anzupassen. Unsere Studien haben gezeigt, dass die Verwendung von individuellen Faktoren die Korrektheit der vorhergesagten Datenschutzeinstellungen erheblich erhöht. Es hat sich gezeigt, dass die Benutzeroberflächen die Anzahl der Fehler, insbesondere versehentliches Teilen von Daten, erheblich verringern