On Collaborative Predictive Blacklisting

Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts (Soldo et al., 2010) and a peer-to-peer one using privacy-preserving data sharing (Freudiger et al., 2015). We show that, while collaboration boosts the number of predicted attacks, it also yields high false positives, ultimately leading to poor accuracy. This motivates us to present a hybrid approach, using a semi-trusted central entity, aiming to increase utility from collaboration while, at the same time, limiting information disclosure and false positives. This leads to a better trade-off of true and false positive rates, while at the same time addressing privacy concerns.Comment: A preliminary version of this paper appears in ACM SIGCOMM's Computer Communication Review (Volume 48 Issue 5, October 2018). This is the full versio

VD-PSI : verifiable delegated private set intersection on outsourced private datasets

Private set intersection (PSI) protocols have many real world applications. With the emergence of cloud computing the need arises for PSI protocols on outsourced datasets where the computation is delegated to the cloud. However, due to the possibility of cloud misbehaviors, it is essential to verify the correctness of any delegated computation, and the integrity of any outsourced datasets. Verifiable Computation on private datasets that does not leak any information about the data is very challenging, especially when the datasets are outsourced independently by different clients. In this paper we present VD-PSI, a protocol that allows multiple clients to outsource their private datasets and delegate computation of set intersection to the cloud, while being able to verify the correctness of the result. Clients can independently prepare and upload their datasets, and with their agreement can verifiably delegate the computation of set intersection an unlimited number of times, without the need to download or maintain a local copy of their data. The protocol ensures that the cloud learns nothing about the datasets and the intersection. VD-PSI is efficient as its verification cost is linear to the intersection cardinality, and its computation and communication costs are linear to the dataset cardinality. Also, we provide a formal security analysis in the standard model

Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values

Two-party secure function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving "friend finder" application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.Comment: 20 pages, shorter conference version published in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 582-596, ACM New York, NY, US

LLAMA: A Low Latency Math Library for Secure Inference

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both the client (holding sensitive input) and the server (holding sensitive weights of the ML model) while realizing inference-as-a-service. Although many specialized protocols exist for this task, including those in the preprocessing model (where a majority of the overheads are moved to an input independent offline phase), they all still suffer from large online complexity. Specifically, the protocol phase that executes once the parties know their inputs, has high communication, round complexity, and latency. Function Secret Sharing (FSS) based techniques offer an attractive solution to this in the trusted dealer model (where a dealer provides input independent correlated randomness to both parties), and 2PC protocols obtained based on these techniques have a very lightweight online phase. Unfortunately, current FSS-based 2PC works (AriaNN, PoPETS 2022; Boyle et al. Eurocrypt 2021; Boyle et al. TCC 2019) fall short of providing a complete solution to secure inference. First, they lack support for math functions (e.g., sigmoid, and reciprocal square root) and hence, are insufficient for a large class of inference algorithms (e.g. recurrent neural networks). Second, they restrict all values in the computation to be of the same bitwidth and this prevents them from benefitting from efficient float-to-fixed converters such as Tensorflow Lite that crucially use low bitwidth representations and mixed bitwidth arithmetic. In this work, we present LLAMA -- an end-to-end, FSS based, secure inference library supporting precise low bitwidth computations (required by converters) as well as provably precise math functions; thus, overcoming all the drawbacks listed above. We perform an extensive evaluation of LLAMA and show that when compared with non-FSS based libraries supporting mixed bitwidth arithmetic and math functions (SIRNN, IEEE S&P 2021), it has at least an order of magnitude lower communication, rounds, and runtimes. We integrate LLAMA with the EzPC framework (IEEE EuroS&P 2019) and demonstrate its robustness by evaluating it on large benchmarks (such as ResNet-50 on the ImageNet dataset) as well as on benchmarks considered in AriaNN -- here too LLAMA outperforms prior work

Feather: Lightweight Multi-party Updatable Delegated Private Set Intersection

With the growth of cloud computing, the need arises for Private Set Intersection (PSI) protocols that can operate on outsourced data and delegate computation to cloud servers. One limitation of existing delegated PSI protocols is that they are all designed for static data and do not allow efficient update on outsourced data. Another limitation is that they cannot efficiently support PSI among multiple clients, which is often needed in practice. This paper presents “Feather”, the first delegated PSI protocol that supports efficient data updates and scalable multi-party PSI computation on outsourced datasets. The clients can independently prepare and upload their private data to the cloud once, then delegate the computation an unlimited number of times. The update operation has O(1) communication and computation complexity, and this is achieved without sacrificing PSI efficiency and security. Feather does not use public key cryptography, that makes it more scalable. We have implemented a prototype and compared the concrete performance against the state of the art. The evaluation indicates that Feather does achieve better performance in both update and PSI computation