Building secure distributed applications the DECENT way
Haofan Zheng, Owen Arden
备注：
17 pages, 21 figures. V3: Added a section to introduce a distributed revoker design; Added details on Decent Handshake protocol; Revised the description on data sealing mechanism; Added a discussion of future research directions related to automatic component verification; Added and replaced 11 figures; V2: Added formal verification result for the protocols; clarified verifiers and revokers
Remote attestation (RA) authenticates code running in trusted execution environments (TEEs), allowing trusted code to be deployed even on untrusted hosts. However, trust relationships established by one component in a distributed application may impact the security of other components, making it difficult to reason about the security of the application as a whole. Furthermore, traditional RA approaches interact badly with modern web service design, which tends to employ small interacting microservices, short session lifetimes, and little or no state.
This paper presents the Decent Application Platform, a framework for building secure decentralized applications. Decent applications authenticate and authorize distributed enclave components using a protocol based on self-attestation certificates, a reusable credential based on RA and verifiable by a third party. Components mutually authenticate each other not only based on their code, but also based on the other components they trust, ensuring that no transitively-connected components receive unauthorized information. While some other TEE frameworks support mutual authentication in some form, Decent is the only system that supports mutual authentication without requiring an additional trusted third party besides the trusted hardware's manufacturer. We have verified the secrecy and authenticity of Decent application data in ProVerif, and implemented two applications to evaluate Decent's expressiveness and performance: DecentRide, a ride-sharing service, and DecentHT, a distributed hash table. On the YCSB benchmark, we show that DecentHT achieves 7.5x higher throughput and 3.67x lower latency compared to a non-Decent implementation.
Decentralised workflow management systems are a new research area, where most work to-date has focused on the system's overall architecture. As little attention has been given to the security aspects in such systems, we follow a security driven approach, and consider, from the perspective of available security building blocks, how security can be implemented and what new opportunities are presented when empowering the decentralised environment with modern distributed security protocols. Our research is motivated by a more general question of how to combine the positive enablers that email exch…
Towards Secure Decentralized Applications and Consensus Protocols in Blockchains (on Selfish Mining, Undercutting Attacks, DAG-Based Blockchains, E-Voting, Cryptocurrency Wallets, Secure-Logging, and CBDC)
Since the inception of blockchain and Bitcoin (Nakamoto (2008)), a decentralized-distributed ledger system and its associated cryptocurrency, respectively, the world has witnessed a slew of newer adaptations and applications. Although the original distributed ledger technology (DLT) of blockchain is deemed secure and decentralized, the confirmation of transactions is inefficient by design. Recently adopted, directed acyclic graph (DAG)-based distributed ledgers validate transactions efficiently without the physically and environmentally costly building process of blocks (Lerner (2015)). Howeve…
The 5G network would fuel next-gen, bandwidth-heavy technologies such as automation, IoT, and AI on the factory floor. It will improve efficiency by powering AR overlays in workflows, as well as ensure safer practices and reduce the number of defects through predictive analytics and real-time detection of damage. The Dynamic Spectrum Sharing (DSS) in 5G networks will permit 5G NR and 4G LTE to coexist and will provide cost-effective and efficient solutions that enable a smooth transition from 4G to 5G. However, this increases the attack surface in the 5G networks. To the best of our knowledge,…
Distributed implementations of access control abound in distributed storage protocols. While such implementations are often accompanied by informal justifications of their correctness, our formal analysis reveals that their correctness can be tricky. In particular, we discover several subtleties in a standard protocol based on capabilities, that can break security under a simple specification of access control. At the same time, we show a sensible refinement of the specification for which a secure implementation of access control is possible. Our models and proofs are formalized in the applied…
The growth in the number of devices connected to the Internet of Things (IoT) poses major challenges in security. The integrity and trustworthiness of data and data analytics are increasingly important concerns in IoT applications. These are compounded by the highly distributed nature of IoT devices, making it infeasible to prevent attacks and intrusions on all data sources. Adversaries may hijack devices and compromise their data. As a result, reactive countermeasures, such as intrusion detection and resilient analytics, become vital components of security. This paper overviews algorithms for…
The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application application over ad hoc clouds is outsourcing computationally intensive problems to nearby cloud agents to solve in a distributed manner.
CSIRO Data61（联邦科学与工业研究组织数据61所）；University of Technology Sydney（悉尼科技大学）；Harbin University of Technology（哈尔滨工业大学）；Zhejiang Lab（之江实验室）
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and l…
Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evalua…
We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton (TKDE 2004). Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. (PDIS 1996), which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms --- one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one pla…
We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-ε)n$ malicious nodes for any constant $1/2 > ε> 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability.
Secure sum computation of private data inputs is an interesting example of Secure Multiparty Computation (SMC) which has attracted many researchers to devise secure protocols with lower probability of data leakage. In this paper, we provide a novel protocol to compute the sum of individual data inputs with zero probability of data leakage when two neighbor parties collude to know the data of a middle party. We break the data block of each party into number of segments and redistribute the segments among parties before the computation. These entire steps create a scenario in which it becomes im…
Matrix multiplication is one of the key operations in various engineering applications. Outsourcing large-scale matrix multiplication tasks to multiple distributed servers or cloud is desirable to speed up computation. However, security becomes an issue when these servers are untrustworthy. In this paper, we study the problem of secure distributed matrix multiplication from distributed untrustworthy servers. This problem falls in the category of secure function computation and has received significant attention in the cryptography community. However, the fundamental limits of information-theor…
Research on data confidentiality, integrity and availability is gaining momentum in the ICT community, due to the intrinsically insecure nature of the Internet. While many distributed systems and services are now based on secure communication protocols to avoid eavesdropping and protect confidentiality, the techniques usually employed in distributed simulations do not consider these issues at all. This is probably due to the fact that many real-world simulators rely on monolithic, offline approaches and therefore the issues above do not apply. However, the complexity of the systems to be simul…
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we present a non-direct secure extension of the recently introduced bivariate polynomial codes. Bivariate polynomial codes have been shown to be able to further speed up distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them whil…
Privacy Preserving Data Mining is a method which ensures privacy of individual information during mining. Most important task involves retrieving information from multiple data bases which is distributed. The data once in the data warehouse can be used by mining algorithms to retrieve confidential information. The proposed framework has two major tasks, secure transmission and privacy of confidential information during mining. Secure transmission is handled by using elliptic curve cryptography and data distortion for privacy preservation ensuring highly secure environment.
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can put sensitive information of study participants at risk. To address the privacy concern, we present a distributed Bayesian learning approach via Preconditioned Stochastic Gradient Langevin Dynamics with RMSprop, which combines differential privacy and homomorphic encryption in a harmonious manner while protecting private information. We applied the proposed secure and privacy-preserving distributed Bayesian learning a…
Cryptography is the discipline that allows securing of the exchange of information. In this internship, we will focus on a certain branch of this discipline, secure computation in a network. The main goal of this internship, illustrated in this report, is to adapt a roster of protocols intended to do linear algebra. We want to adapt them to do algebra for matrices with polynomial coefficients. We then wish to make a complete analysis of the different complexities of these protocols.
Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on various agents to perform multiple distributed training tasks. Considering the decentralized computing topology, privacy has become a first-class concern. Moreover, assuming limited information processing capability for the agents calls for a sophisticated \textit{privacy-preser…
Coordinated Sciences Laboratory（协同科学实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem. Robust aggregation, the standard defense for Byzantine attacks, generally requires server access to individual updates or nonlinear computation -- thus is incompatible with privacy-preserving methods such as secure aggregation via multiparty computation. To this end, we propose SHARE (Secure Hierarchical Robust Aggregation), a distributed learning framework designed to cryptographically preserve client update privacy and robustness to Byzantine adversaries simultaneously. The key id…
