NegDL: Privacy-Preserving Deep Learning Based on Negative Database

In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database (\textit{NDB}) is a new type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on \textit{NDB}. Specifically, private data are first converted to \textit{NDB} as the input of deep learning models by a generation algorithm called \textit{QK}-hidden algorithm, and then the sketches of \textit{NDB} are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL could be comparable to the original deep learning model in most cases, and it performs better than the method based on differential privacy

Protecting Data Privacy through Hard-to-Reverse Negative Databases

Abstract A set DB of data elements can be represented in terms of its complement set, known as a negative database. That is, all of the elements not in DB are represented, and DB itself is not explicitly stored. This method of representing data has certain properties that are relevant for privacy enhancing applications. The paper reviews the negative database (NDB) representation scheme for storing a negative image compactly, and it proposes using a collection of NDBs to represent a single DB, that is, one NDB is assigned for each record in DB. This method has the advantage of producing negative databases that are hard to reverse in practice, i.e., from which it is hard to obtain DB. This result is obtained by adapting a technique for generating hard-to-solve 3-SAT formulas. Finally we suggest potential avenues of application

A hierarchical group model for programming sensor networks

A hierarchical group model that decouples computation from hardware can characterize and aid in the construction of sensor network software with minimal overhead. Future sensor network applications will move beyond static, homogeneous deployments to include dynamic, heterogeneous elements. These sensor networks will also gain new users, including casual users who will expect intuitive interfaces to interact with sensor networks. To address these challenges, a new computational model and a system implementing the model are presented. This model ensures that computations can be readily reassigned as sensor nodes are introduced or removed. The model includes methods for communication to accommodate these dynamic elements. This dissertation presents a detailed description and design of a computational model that resolves these challenges using a hierarchical group mechanism. In this model, computation is tasked to logical groups and split into collective and local components that communicate hierarchically. Local computation is primarily used for data production and publishes data to the collective computation. Similarly, collective computation is primarily used for data aggregation and pushes results back to the local computation. Finally, the model includes data-processing functions interposed between local and collective functions that are responsible for data conversion. This dissertation also presents implementations and applications of the model. Implementations include Kensho, a C-based implementation of the hierarchical group model, that can be used for a variety of user applications. Another implementation, Tables, presents a spreadsheet-inspired view of the sensor network that takes advantage of hierarchical groups for both computation and communication. Users are able to specify both local and collective functions that execute on the sensor network via the spreadsheet interface. Applications of the model are also explored. One application, FUSN, provides a set of methods for constructing filesystem-based interfaces for sensor networks. This demonstrates the general applicability of the model as applied to sensor network programming and management interfaces. Finally, the model is applied to a novel privacy algorithm to demonstrate that the model isn\u27t strictly limited to programming interfaces

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp