Waldo: A Private Time-Series Database from Function Secret Sharing

Dauterman, Emma; Rathee, Mayank; Popa, Raluca Ada; Stoica, Ion

doi:10.1109/sp46214.2022.9833611

Cited by 26 publications

(7 citation statements)

References 58 publications

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“…More recently, Liu et al [76] employed secure multi-party computation (SMC) techniques for privacypreserving collaborative medical time-series analysis, based on dynamic time warping. Dauterman et al [31] use function secret sharing (FSS) to support various functionalities, e.g., multi-predicate filtering, on private time-series databases. All these authors focus on simple collaborative time-series tasks and not on the privacy-preserving training of machine learning models on time-series data, which is the main focus of this work.…”

Section: A Privacy-preserving Time-seriesmentioning

confidence: 99%

Privacy-Preserving Federated Recurrent Neural Networks

Sav¹,

Abdulrahman²,

Pyrgelis³

et al. 2022

Preprint

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We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a federated learning setting by relying on multiparty homomorphic encryption (MHE). RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates the federated learning attacks that target the gradients under a passive-adversary threat model. We propose a novel packing scheme, multi-dimensional packing, for a better utilization of Single Instruction, Multiple Data (SIMD) operations under encryption. With multi-dimensional packing, RHODE enables the efficient processing, in parallel, of a batch of samples. To avoid the exploding gradients problem, we also provide several clip-by-value approximations for enabling gradient clipping under encryption. We experimentally show that the model performance with RHODE remains similar to non-secure solutions both for homogeneous and heterogeneous data distribution among the data holders. Our experimental evaluation shows that RHODE scales linearly with the number of data holders and the number of timesteps, sub-linearly and sub-quadratically with the number of features and the number of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is the first system that provides the building blocks for the training of RNNs and its variants, under encryption in a federated learning setting.

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Section: A Privacy-preserving Time-seriesmentioning

confidence: 99%

Privacy-Preserving Federated Recurrent Neural Networks

Sav¹,

Abdulrahman²,

Pyrgelis³

et al. 2022

Preprint

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“…It is recalled that in the attributed graph encryption phase, each value demanding protection is encoded into a one-hot vector. The adoption of such encoding strategy, inspired by [39], [42], is actually useful in providing an alternative way to avoid fresh FSS keys in OblivGM for evaluating the same predicate on different attribute values. At a high level, the idea is that with such encoding, FSS keys can be evaluated against the public locations of entries in one-hot vectors.…”

Section: Secure Query Token Generationmentioning

confidence: 99%

“…On another hand, as the one-hot vectors are protected under the RSS technique in OblivGM, the above idea has to be instantiated over the RSS-protected one-hot vectors. Inspired by [42], OblivGM leverages the replication property of RSS and constructs three pairs of FSS keys for each private predicate so as to bridge FSS and RSS. How this can actually work out will become more clear in the subsequent phase of secure attributed subgraph matching, which will be introduced shortly in Section V-D.…”

Section: Secure Query Token Generationmentioning

confidence: 99%

OblivGM: Oblivious Attributed Subgraph Matching as a Cloud Service

Wang

Zheng

Jia

et al. 2022

IEEE Trans.Inform.Forensic Secur.

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In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured data in various applications. Such trend of outsourced graph analytics, however, is accompanied with critical privacy concerns regarding the information-rich and proprietary attributed graph data. In light of this, we design, implement, and evaluate OblivGM, a new system aimed at oblivious graph analytics services outsourced to the cloud. OblivGM focuses on the support for attributed subgraph matching, one popular and fundamental graph query functionality aiming to retrieve from a large attributed graph subgraphs isomorphic to a small query graph. Built from a delicate synergy of insights from attributed graph modelling and advanced lightweight cryptography, OblivGM protects the confidentiality of data content associated with attributed graphs and queries, conceals the connections among vertices in attributed graphs, and hides search access patterns. Meanwhile, OblivGM flexibly supports oblivious evaluation of varying subgraph queries, which may contain equality and/or range predicates. Extensive experiments over a real-world attributed graph dataset demonstrate that while providing strong security guarantees, OblivGM achieves practically affordable performance (with query latency on the order of a few seconds).

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“…In the past few years, we've seen an explosion of academic and industrial cryptographic systems built on distributed trust; some applications are based on secure multi-party computation [9,34,86] (e.g. private search [23,24,64,82], private analytics [7,11,19,27,42] private media delivery [38], private blocklist lookups [46], private DNS [72], anonymous messaging [17,20,28,48,49,84], and cryptocurrency wallets [30,31,45,63,67,70,75]), while others are based on Byzantine fault-tolerant consensus and blockchains [6,8,15,25,36,37,47,50,56,88].…”

Section: Introductionmentioning

confidence: 99%

“…Distributed-trust systems only provide strong security guarantees insofar as there is no central point of attack that allows an attacker to compromise more than an application-specific threshold of parties. Many academic works simply assume the existence of multiple non-colluding servers [20,23,24,28,38,38,46,82], but an application developer that wants to deploy a distributed-trust system faces difficult questions: who should have administrative control over the different trust domains, and how do you convince another party that you do not control to run your system? We study the difficulties developers have faced when deploying distributed-trust systems in §2.…”

Section: Introductionmentioning

confidence: 99%

Reflections on trusting distributed trust

Dauterman,

Fang,

Crooks

et al. 2022

Preprint

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Many systems today distribute trust across multiple parties such that the system provides certain security properties if a subset of the parties are honest. In the past few years, we have seen an explosion of academic and industrial cryptographic systems built on distributed trust, including secure multi-party computation applications (e.g., private analytics, secure learning, and private key recovery) and blockchains. These systems have great potential for improving security and privacy, but face a significant hurdle on the path to deployment. We initiate study of the following problem: a single organization is, by definition, a single party, and so how can a single organization build a distributed-trust system where corruptions are independent? We instead consider an alternative formulation of the problem: rather than ensuring that a distributed-trust system is set up correctly by design, what if instead, users can audit a distributed-trust deployment? We propose a framework that enables a developer to efficiently and cheaply set up any distributed-trust system in a publicly auditable way. To do this, we identify two application-independent building blocks that we can use to bootstrap arbitrary distributed-trust applications: secure hardware and an append-only log. We show how to leverage existing implementations of these building blocks to deploy distributed-trust systems, and we give recommendations for infrastructure changes that would make it easier to deploy distributed-trust systems in the future. CCS Concepts• Security and privacy → Distributed systems security;

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Waldo: A Private Time-Series Database from Function Secret Sharing

Cited by 26 publications

References 58 publications

Privacy-Preserving Federated Recurrent Neural Networks

Privacy-Preserving Federated Recurrent Neural Networks

OblivGM: Oblivious Attributed Subgraph Matching as a Cloud Service

Reflections on trusting distributed trust

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