PapersCut A shortcut to recent security papers

GraphDefense: Towards Robust Graph Convolutional Networks

Authors: Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh

Abstract: In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the good performance of GCNs on graph semi-supervised learning tasks, previous works have shown that the original GCNs are very unstable to adversarial perturbations. In particular, we can observe a severe performance degradation by slightly changing the graph adjacency matrix or the features of a few nodes, making it unsuitable for security-critical applications. Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations. In addition, for our defense method, we could still maintain semi-supervised learning settings, without a large label rate. We also show that adversarial training in features is equivalent to adversarial training for edges with a small perturbation. Our experiments show that the proposed defense methods successfully increase the robustness of Graph Convolutional Networks. Furthermore, we show that with careful design, our proposed algorithm can scale to large graphs, such as Reddit dataset.

Date: 11 Nov 2019

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DRAB-LOCUS: An Area-Efficient AES Architecture for Hardware Accelerator Co-Location on FPGAs

Authors: Jacob T. Grycel, Robert J. Walls

Abstract: Advanced Encryption Standard (AES) implementations on Field Programmable Gate Arrays (FPGA) commonly focus on maximizing throughput at the cost of utilizing high volumes of FPGA slice logic. High resource usage limits systems' abilities to implement other functions (such as video processing or machine learning) that may want to share the same FPGA resources. In this paper, we address the shared resource challenge by proposing and evaluating a low-area, but high-throughput, AES architecture. In contrast to existing work, our DSP/RAM-Based Low-CLB Usage (DRAB-LOCUS) architecture leverages block RAM tiles and Digital Signal Processing (DSP) slices to implement the AES Sub Bytes, Mix Columns, and Add Round Key sub-round transformations, reducing resource usage by a factor of 3 over traditional approaches. To achieve area-efficiency, we built an inner-pipelined architecture using the internal registers of block RAM tiles and DSP slices. Our DRAB-LOCUS architecture features a 12-stage pipeline capable of producing 7.055 Gbps of interleaved encrypted or decrypted data, and only uses 909 Look Up tables, 593 Flip Flops, 16 block RAMs, and 18 DSP slices in the target device.

Comment: 16 pages, initial submission

Date: 11 Nov 2019

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Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces

Authors: Takao Murakami, Koki Hamada, Yusuke Kawamoto, Takuma Hatano

Abstract: With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting users' privacy. Although location synthesizers have been widely studied, existing synthesizers do not provide utility, privacy, or scalability sufficiently, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We simultaneously factorize these two tensors via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors using the MH (Metropolis-Hastings) algorithm. We comprehensively evaluate the proposed method using two datasets. Our experimental results show that the proposed method preserves various statistical features, provides plausible deniability, and synthesizes large-scale location traces in practical time. The proposed method also significantly outperforms the state-of-the-art with regard to the utility, privacy, and scalability.

Date: 11 Nov 2019

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Privacy-Preserving Gradient Boosting Decision Trees

Authors: Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He

Abstract: The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. \textit{Sensitivity} and \textit{privacy budget} are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines.

Comment: Accepted by AAAI-20

Date: 11 Nov 2019

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Just Enough Security: Reducing Proof-of-Work Ecological Footprint

Authors: Itay Tsabary, Alexander Spiegelman, Ittay Eyal

Abstract: Proof-of-work (PoW) mechanisms secure about~80\% of the \$250B cryptocurrency market. PoW requires system participants to expend computational resources, and protects the system from attackers who cannot expend resources at an equivalent rate. These systems operate in the~\emph{permissionless} setting and compensate their users with cryptocurrency, having a monetary value. As cryptocurrency prices sore so do the invested resources, and Bitcoin expenditures alone are~0.24\% of the global electricity consumption. Arguably, this is superfluous, and lowering the ecological footprint justifies settling for a lower attack threshold. We present novel protocols that allow the system designer to accurately trade off security for expenditure reduction. To the best of our knowledge, this is the first work to do so without adding qualitatively stronger model assumptions. Moreover, our protocols reduce PoW resource expenditure significantly, but with only limited security degradation. To analyze these protocols We refine the common blockchain model to take into account the cryptocurrency value in real terms, expenditure, and security metrics, distinguishing common revenue-seeking attacks from sabotage. Our analysis of game-theoretic and economical properties of the protocols can be used to tune blockchain security to its required level and limit its ecological damage.

Date: 11 Nov 2019

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Authentication of Smartphone Users Using Behavioral Biometrics

Authors: Abdulaziz Alzubaidi, Jugal Kalita

Abstract: Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work, and socialize whenever and wherever they want. Smartphones are small in size, so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users' private lives. In addition, this might enable pervasive observation or imitation of one's movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.

Date: 11 Nov 2019

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Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys

Authors: Asma Aloufi, Peizhao Hu

Abstract: Homomorphic encryption (HE) is a promising cryptographic technique for enabling secure collaborative machine learning in the cloud. However, support for homomorphic computation on ciphertexts under multiple keys is inefficient. Current solutions often require key setup before any computation or incur large ciphertext size (at best, grow linearly to the number of involved keys). In this paper, we proposed a new approach that leverages threshold and multi-key HE to support computations on ciphertexts under different keys. Our new approach removes the need for key setup between each client and the set of model owners. At the same time, this approach reduces the number of encrypted models to be offloaded to the cloud evaluator, and the ciphertext size with a dimension reduction from (N+1)x2 to 2x2. We present the details of each step and discuss the complexity and security of our approach.

Comment: 8 pages, 2 figures, In International Workshop on Privacy Engineering (IWPE'19), co-located with IEEE Symposium on Security and Privacy (S&P'19)

Date: 11 Nov 2019

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GRuB: Gas-Efficient Blockchain Storage via Workload-Adaptive Data Replication

Authors: Kai Li, Yuzhe, Tang, Qi, Zhang, Cheng Xu, Jianliang Xu

Abstract: Modern Blockchains support the execution of user programs, called smart contracts. As a trusted computing platform, smart contracts bring decentralization, computation integrity, open access and information transparency to average users on the Internet. However, running smart-contract programs leads to high costs, known as Gas. Such costs prevent the use of smart contracts in data-intensive application scenarios, such as high-frequency trading and transparency logging. This paper addresses the Gas-based cost effectiveness in the most consuming layer of a smart contract, namely data storage. We present GRuB, a dynamic data-replication framework that monitors the smart-contract workload and makes online replication decisions. A new online algorithm is proposed that provides constant-bounded 'competitiveness' in Gas. To further save Gas, the workload monitor and decision maker are run off the Blockchain and with security against the forging of workload trace being monitored. A GRuB prototype is built, including a smart-contract component on Ethereum and an off-chain middleware on top of Google LevelDB. The cost evaluation under the YCSB workloads shows that GRuB can converge quickly to changing workloads and save Gas significantly compared with static replication schemes. Two case studies are conducted for data-intensive applications, including high-frequency trading and transparency logging, in which running GRuB leads to affordable Gas.

Comment: Blockchain storage replication, GRuB, 15 pages

Date: 11 Nov 2019

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Neural Cryptanalysis: Metrics, Methodology, and Applications in CPS Ciphers

Authors: Ya Xiao, Qingying Hao, Danfeng, Yao

Abstract: Many real-world cyber-physical systems (CPS) use proprietary cipher algorithms. In this work, we describe an easy-to-use black-box security evaluation approach to measure the strength of proprietary ciphers without having to know the algorithms. We quantify the strength of a cipher by measuring how difficult it is for a neural network to mimic the cipher algorithm. We define new metrics (e.g., cipher match rate, training data complexity and training time complexity) that are computed from neural networks to quantitatively represent the cipher strength. This measurement approach allows us to directly compare the security of ciphers. Our experimental demonstration utilizes fully connected neural networks with multiple parallel binary classifiers at the output layer. The results show that when compared with round-reduced DES, the security strength of Hitag2 (a popular stream cipher used in the keyless entry of modern cars) is weaker than 3-round DES.

Comment: 8 pages, 8 figures, The 2019 IEEE Conference on Dependable and Secure Computing

Date: 11 Nov 2019

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Interaction is necessary for distributed learning with privacy or communication constraints

Authors: Yuval Dagan, Vitaly Feldman

Abstract: Local differential privacy (LDP) is a model where users send privatized data to an untrusted central server whose goal it to solve some data analysis task. In the non-interactive version of this model the protocol consists of a single round in which a server sends requests to all users then receives their responses. This version is deployed in industry due to its practical advantages and has attracted significant research interest. Our main result is an exponential lower bound on the number of samples necessary to solve the standard task of learning a large-margin linear separator in the non-interactive LDP model. Via a standard reduction this lower bound implies an exponential lower bound for stochastic convex optimization and specifically, for learning linear models with a convex, Lipschitz and smooth loss. These results answer the questions posed in \citep{SmithTU17,DanielyF18}. Our lower bound relies on a new technique for constructing pairs of distributions with nearly matching moments but whose supports can be nearly separated by a large margin hyperplane. These lower bounds also hold in the model where communication from each user is limited and follow from a lower bound on learning using non-adaptive \emph{statistical queries}.

Date: 11 Nov 2019

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