Ethan Cecchetti, Fan Zhang, Yan Ji, Ahmed E. Kosba · 6 authors
Blockchains and more general distributed ledgers are becoming increasingly popular as efficient, reliable, and persistent records of data and transactions. Unfortunately, they ensure reliability and correctness by making all data public, raising confidentiality concerns that eliminate many potential uses.
The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.
Public blockchain networks using proof of work (PoW)-based consensus protocols are considered as a promising platform for decentralized resource management with financial incentive mechanisms. In order to maintain a secured, universal state of the blockchain, PoW-based consensus protocols financially incentivize the nodes in the network to compete for the privilege of block generation through cryptographic puzzle solving. For rational consensus nodes, i.e., miners with limited local computational resources, offloading the computation load for PoW to the cloud/fog providers (CFPs) becomes a viable option. In this paper, we study the interaction between the CFPs and the miners in a PoW-based blockchain network using a game theoretic approach. In particular, we propose a lightweight infrastructure of the PoW-based blockchains, where the computation-intensive part of the consensus process is offloaded to the cloud/fog. We formulate the computation resource management in the blockchain consensus process as a two-stage Stackelberg game, where the profit of the CFP and the utilities of the individual miners are jointly optimized. In the first stage of the game, the CFP sets the price of offered computing resource. In the second stage, the miners decide on the amount of service to purchase accordingly. We apply backward induction to analyze the subgame perfect equilibria in each stage for both uniform and discriminatory pricing schemes. For uniform pricing where the same price applies to all miners, the uniqueness of the Stackelberg equilibrium is validated by identifying the best response strategies of the miners. For discriminatory pricing where the different prices are applied, the uniqueness of the Stackelberg equilibrium is proved by capitalizing on the variational inequality theory. Further, the real experimental results are employed to justify our proposed model.
Remo Manuel Frey, Pascal Bühler, Alexander Gerdes, Thomas Hardjono · 6 authors
In light of digitalization, customers increasingly share private data through their online behaviors and actions. Yet, customers have become reluctant to share data due to privacy concerns. From a psychological perspective, a reduction of users' perceived risks should result in a higher willingness to share sensitive data. The development of blockchain-supported, multi-part computation thereby represents an interesting novel empirical context to study such willingness to disclose personal data, as such technologies involve a privacy-preserving approach that could not only technically solve privacy issues but also ought to address precisely the user's risk perception. Therefore, we conducted an online experiment with 420 participants to examine the willingness to disclose personal data dependent on different privacy protection mechanisms. A deception based experiment allowed to measure not only user intention, but also real user behavior. Surprisingly, our results demonstrate that participants shared similar amounts of personal data for blockchain-supported approaches and standard privacy policies. Even though an aversion to the blockchain system due to its novelty and potentially perceived complexity was not detected. Furthermore, we found that the willingness to share data increased significantly specifically for technically affine people when they were presented with the opportunity to monetize their data. We further discuss the effects of privacy awareness and whether prior knowledge of blockchain technology had a supporting effect for user acceptance.
David Froelicher, Patricia Egger, João Sá Sousa, Jean Louis Raisaro · 8 authors
Abstract Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present U n L ynx , a new decentralized system for efficient privacy-preserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. U n L ynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, U n L ynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. U n L ynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that U n L ynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.
Secret sharing is an important component of cryptography protocols and has a wide range of practical applications. However, the existing secret sharing schemes cannot apply to computationally weak devices and cannot efficiently guarantee fairness. In this study, a novel outsourcing secret sharing scheme is proposed. In the setting of outsourcing secret sharing, clients only need a small amount of decryption and verification operations, while the expensive reconstruction computation and verifiable computation can be outsourced to cloud service providers (CSP). The scheme does not require complex interactive argument or zero‐knowledge proof. The malicious behaviour of clients and CSP can be detected in time. Moreover, the CSP cannot get any useful information about the secret, and it is fair for every client to obtain the secret. At the end of this study, the authors prove the security of the proposed scheme and compare it with other secret sharing schemes.
Abstract The I owe you (IOU) credit network Ripple is one of the most prominent alternatives in the burgeoning field of decentralized payment systems. Ripple’s path-based transactions set it apart from cryptocurrencies such as Bitcoin. Its pseudonymous nature, while still maintaining some regulatory capabilities, has motivated several financial institutions across the world to use Ripple for processing their daily transactions. Nevertheless, with its public ledger, a credit network such as Ripple is no different from a cryptocurrency in terms of weak privacy; recent demonstrative deanonymization attacks raise important concerns regarding the privacy of the Ripple users and their transactions. However, unlike for cryptocurrencies, there is no known privacy solution compatible with the existing credit networks such as Ripple. In this paper, we present PathShuffle, the first path mixing protocol for credit networks. PathShuffle is fully compatible with the current credit networks. As its essential building block, we propose PathJoin, a novel protocol to perform atomic transactions in credit networks. Using PathJoin and the P2P mixing protocol DiceMix, PathShuffle is a decentralized solution for anonymizing path-based transactions. We demonstrate the practicality of PathShuffle by performing path mixing in Ripple.
The recent approval of the General Data Protection Regulation (GDPR) imposes new data protection requirements on data controllers and processors with respect to the processing of European Union (EU) residents' data. These requirements consist of a single set of rules that have binding legal status and should be enforced in all EU member states. In light of these requirements, we propose in this paper the use of a blockchain-based approach to support data accountability and provenance tracking. Our approach relies on the use of publicly auditable contracts deployed in a blockchain that increase the transparency with respect to the access and usage of data. We identify and discuss three different models for our approach with different granularity and scalability requirements where contracts can be used to encode data usage policies and provenance tracking information in a privacy-friendly way. From these three models we designed, implemented, and evaluated a model where contracts are deployed by data subjects for each data controller, and a model where subjects join contracts deployed by data controllers in case they accept the data handling conditions. Our implementations show in practice the feasibility and limitations of contracts for the purposes identified in this paper.
Sensor networks and Wireless Sensor Networks (WSN) are key components for the development of the Internet of Things. These networks are subject of two kinds of constraints. Adaptability by the mean of mutability and evolutivity, and constrained node resources such as energy consumption, computational complexity or memory usage. In this context, none of the existing protocols and models allows reliable peer authentication and trust level management. In the field of virtual economic transactions, Bitcoin has proposed a new decentralized and evolutive way to model and acknowledge trust and data validity in a peer network by the mean of the blockchain. We propose a new security model and its protocol based on the blockchain technology to ensure validity and integrity of cryptographic authentication data and associate peer trust level, from the beginning to the end of the sensor network lifetime.
Bitcoin and other cryptocurrencies have surged in popularity over the last decade. Although Bitcoin does not claim to provide anonymity for its users, it enjoys a public perception of being a privacy preserving financial system. In reality, cryptocurrencies publish users' entire transaction histories in plaintext, albeit under a pseudonym; this is required for transaction validation. Therefore, if a user's pseudonym can be linked to their human identity, the privacy fallout can be significant. Recently, researchers have demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin network's peer-to-peer (P2P) networking protocols. In particular, the P2P network currently forwards content in a structured way that allows observers to deanonymize users. In this work, we redesign the P2P network from first principles with the goal of providing strong, provable anonymity guarantees. We propose a simple networking policy called Dandelion which provides quasi-optimal, network-wide anonymity, with minimal cost to the network's utility. We also discuss practical implementation challenges and propose heuristic solutions.
Hossein Shafagh, Lukas Burkhalter, Anwar Hithnawi, Simon Duquennoy
Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with data ownership. Our design is tailored for IoT data streams and enables secure data sharing. We enable a secure and resilient access control management, by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. We facilitate the storage of time-series IoT data at the edge of the network via a locality-aware decentralized storage system that is managed with the blockchain technology. Our system is agnostic of the physical storage nodes and supports as well utilization of cloud storage resources as storage nodes.
Disseminating medical data beyond the protected cloud of institutions poses severe risks to patients’ privacy, as breaches push them to the point where they abstain from full disclosure of their condition. This situation negatively impacts the patient, scientific research, and all stakeholders. To address this challenge, we propose a blockchain-based data sharing framework that sufficiently addresses the access control challenges associated with sensitive data stored in the cloud using immutability and built-in autonomy properties of the blockchain. Our system is based on a permissioned blockchain which allows access to only invited, and hence verified users. As a result of this design, further accountability is guaranteed as all users are already known and a log of their actions is kept by the blockchain. The system permits users to request data from the shared pool after their identities and cryptographic keys are verified. The evidence from the system evaluation shows that our scheme is lightweight, scalable, and efficient.
Malte Möser, Kyle Soska, Ethan Heilman, Kevin Lee · 11 authors
Abstract Monero is a privacy-centric cryptocurrency that allows users to obscure their transactions by including chaff coins, called “mixins,” along with the actual coins they spend. In this paper, we empirically evaluate two weaknesses in Monero’s mixin sampling strategy. First, about 62% of transaction inputs with one or more mixins are vulnerable to “chain-reaction” analysis - that is, the real input can be deduced by elimination. Second, Monero mixins are sampled in such a way that they can be easily distinguished from the real coins by their age distribution; in short, the real input is usually the “newest” input. We estimate that this heuristic can be used to guess the real input with 80% accuracy over all transactions with 1 or more mixins. Next, we turn to the Monero ecosystem and study the importance of mining pools and the former anonymous marketplace AlphaBay on the transaction volume. We find that after removing mining pool activity, there remains a large amount of potentially privacy-sensitive transactions that are affected by these weaknesses. We propose and evaluate two countermeasures that can improve the privacy of future transactions.
The blockchain has fueled one of the most enthusiastic bursts of activity in applied cryptography in years, but outstanding problems in security and privacy research must be solved for blockchain technologies to go beyond the hype and reach their full potential. At the first IEEE Privacy and Security on the Blockchain Workshop (IEEE S&B), we presented peer-reviewed papers bringing together academia and industry to analyze problems ranging from deploying newer cryptographic primitives on Bitcoin to enabling usecases like privacy-preserving file storage. We overview not only the larger problems the workshop has set out to tackle, but also outstanding unsolved issues that will require further cooperation between academia and the blockchain community.
Bitcoin is a popular alternative to fiat money, widely used for its perceived anonymity properties. However, recent attacks on Bitcoin's peer-to-peer (P2P) network demonstrated that its gossip-based flooding protocols, which are used to ensure global network consistency, may enable user deanonymization---the linkage of a user's IP address with her pseudonym in the Bitcoin network. In 2015, the Bitcoin community responded to these attacks by changing the network's flooding mechanism to a different protocol, known as diffusion. However, no systematic justification was provided for the change, and it is unclear if diffusion actually improves the system's anonymity. In this paper, we model the Bitcoin networking stack and analyze its anonymity properties, both pre- and post-2015. In doing so, we consider new adversarial models and spreading mechanisms that have not been previously studied in the source-finding literature. We theoretically prove that Bitcoin's networking protocols (both pre- and post-2015) offer poor anonymity properties on networks with a regular-tree topology. We validate this claim in simulation on a 2015 snapshot of the real Bitcoin P2P network topology.
This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.
Tyler Crain, Vincent Gramoli, Mikel Larrea, Michel Raynal
This paper introduces a deterministic Byzantine consensus algorithm that relies on a new weak coordinator. As opposed to previous algorithms that cannot terminate in the presence of a faulty or slow coordinator, our algorithm can terminate even when its coordinator is faulty, hence the name weak coordinator. The key idea is to allow processes to complete asynchronous rounds as soon as they receive a threshold of messages, instead of having to wait for a message from a coordinator that may be slow. The resulting algorithm assumes partial synchrony, is resilience optimal, time optimal and does not need signatures. Our presentation is didactic: we first present a simple safe binary Byzantine consensus algorithm, modify it to ensure termination, and finally present an optimized reduction from multivalue consensus to binary consensus that may terminate in 4 message delays. To evaluate our algorithm, we deployed it on 100 machines distributed in 5 datacenters across different continents and compared its performance against the randomized solution from Mostefaoui, Moumem and Raynal [PODC14] that terminates in O(1) rounds in expectation. Our algorithm always outperforms the latter even in the presence of Byzantine behaviors. Our algorithm has a subsecond average latency in most of our geo-distributed experiments, even when attacked by a well-engineered coalition of Byzantine processes.
Bitcoin and other cryptocurrencies have surged in popularity over the last decade. Although Bitcoin does not claim to provide anonymity for its users, it enjoys a public perception of being a `privacy-preserving' financial system. In reality, cryptocurrencies publish users' entire transaction histories in plaintext, albeit under a pseudonym; this is required for transaction validation. Therefore, if a user's pseudonym can be linked to their human identity, the privacy fallout can be significant. Recently, researchers have demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin network's peer-to-peer (P2P) networking protocols. In particular, the P2P network currently forwards content in a structured way that allows observers to deanonymize users. In this work, we redesign the P2P network from first principles with the goal of providing strong, provable anonymity guarantees. We propose a simple networking policy called Dandelion, which achieves nearly-optimal anonymity guarantees at minimal cost to the network's utility. We also provide a practical implementation of Dandelion.
Mathieu Chanson, Alexander Bogner, Felix Wortmann, Elgar Fleisch
Giving people ownership of the data they produce becomes more and more important in times of ever-growing capabilities to collect and analyze data of individuals. In light of this challenge, we show how blockchain technology can enable privacy by presenting an odometer fraud prevention system. It records mileage and GPS data of cars and secures that on the blockchain, which strongly hinders odometer fraud. Our users own and control their data while at the same time data integrity is ensured. This facilitates the certification of that data. We discuss the advantages of this approach compared to current systems and also highlight limitations of our architecture and the use of blockchain technology.
In this paper a novel feedback controller and stability analysis of a blockchain implementation is developed by using a control engineering perspective. The controller output equals the difficulty adjustment in the mining process while the feedback variable is the average block time over a certain time period. The computational power (hash rate) of the miners is considered a disturbance in the model. The developed controller is tested against a simulation model with constant disturbance, step and ramp responses as well as with a high-frequency sinusoidal disturbance. Stability and a fast response is demonstrated in all these cases with a controller which adjusts it's output at every new block. Finally the performance of the controller is implemented and demonstrated on a testnet with a constant hash rate as well as on the mainnet of a public open source blockchain project.
Within the phenomenon known as the Internet of Things (IoT), an enormous growth is taking place. IoT systems exist in different ways, ranging from industrial applications to user focused systems. A specific subset of a user-focused IoT system is found as Smart Home environments. At Smart Homes, themultiple Smart Objects or Smart Devices are working together, frequently based on sensor input, to increase the comfort and user experience of the home inhabitant(s) and guest(s). Smart Objects can have automated tasks, home security enabling functions or efficiency improving functionality. Apart from great applications of Smart Home devices, threats from a cyber security perspective are present: cyber risks arise due to a variety of threats on such IoT systems. We show that in the development of new Smart Home products or systems, vendors fail to meet requirements for security and privacy are not met. Comparing the current state of the market, the four most used Smart Home ecosystems (Samsung Smartthings, Apple Homekit, Amazon Echo and IFTTT) are surveyed based on three key focus areas: 1. The regulatory compliance of the systems according to the upcoming General Data Protection Regulation (GDPR). 2. The commercial threats due to data profiling. 3. The risk of data leaks due to insufficient security. This analysis results in four key observations: 1. Security- and Privacy-By-Design is usually not in place due to the fact that the focus lies on launching a product as soon as possible, e.g. due to market competition; 2. Vendors process (meta)data on the vendors locations resulting in data profiling, which can compromise user privacy; 3. Smart Home ecosystems are not ready for the GDPR; 4. A trade off between privacy, security and utility usually results to the detriment of the first two and favors the latter. We propose a new design for a Smart Home ecosystem. In this design, the focus lies at the privacy of the end-user. We design a network for device-fitting encrypted communication between Smart Devices and User Devices and the Privacy Enforcing Arbiter (or Peter). Peter functions like a hub in the network, managing among others all traffic, user privileges and key distribution. With Peter, the centralized cloud party (vendor) for data storage and data analysis is replaced with a decentralized personal storage and computation entity at home. With our network design, we facilitate the use of IoT devices in home in a privacy-friendly way. Within the network, devices are authenticated using PhysicallyUnclonable Function technology and users are authenticated with a Zero Knowledge Proof. We analyze the privacy and security of our proposed network, based on a series of possible cyber attacks and the upcoming GDPR. Furthermore, we analyze the computational complexity and scalability of the network, based on market conform device power.