Distributional differential privacy for large-scale smart metering

There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use. As a result, these systems do not provide meaningful privacy guarantees over long time scales. Moreover, existing techniques to mitigate this effect do not apply in the “local model” of differential privacy that these systems use. In this paper, we introduce a new technique for local differential privacy that makes it possible to maintain up-to-date statistics over time, with privacy guarantees that degrade only in the number of changes in the underlying distribution rather than the number of collection periods. We use our technique for tracking a changing statistic in the setting where users are partitioned into an unknown collection of groups, and at every time period each user draws a single bit from a common (but changing) group-specific distribution. We also provide an application to frequency and heavy-hitter estimation.

Download Full-text

A Clonal Selection Optimization System for Multiparty Secure Computing

Complexity ◽

10.1155/2021/7638394 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Minyu Shi ◽

Yongting Zhang ◽

Huanhuan Wang ◽

Junfeng Hu ◽

Xiang Wu

Keyword(s):

Deep Learning ◽

Large Scale ◽

Differential Privacy ◽

Clonal Selection ◽

Smart Cities ◽

Model Performance ◽

Processing System ◽

Optimization System ◽

Learning Scheme ◽

Training Efficiency

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

Download Full-text

Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Security and Communication Networks ◽

10.1155/2021/6665984 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jing Zhao ◽

Shubo Liu ◽

Xingxing Xiong ◽

Zhaohui Cai

Keyword(s):

Time Series ◽

Privacy Protection ◽

Large Scale ◽

Differential Privacy ◽

Time Series Data ◽

Sliding Window ◽

Data Publishing ◽

Series Data ◽

Data Publication ◽

Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

Download Full-text

Frame-Type-Aware Static Time Slotted Channel Hopping Scheduling Scheme for Large-Scale Smart Metering Networks

IEEE Access ◽

10.1109/access.2018.2886375 ◽

2019 ◽

Vol 7 ◽

pp. 2200-2209 ◽

Cited By ~ 1

Author(s):

Huiung Park ◽

Haeyong Kim ◽

Kyeong Tae Kim ◽

Seon-Tae Kim ◽

Pyeongsoo Mah

Keyword(s):

Large Scale ◽

Smart Metering ◽

Scheduling Scheme ◽

Channel Hopping

Download Full-text

A Technique to provide differential privacy for appliance usage in smart metering

Information Sciences ◽

10.1016/j.ins.2016.08.011 ◽

2016 ◽

Vol 370-371 ◽

pp. 355-367 ◽

Cited By ~ 23

Author(s):

Pedro Barbosa ◽

Andrey Brito ◽

Hyggo Almeida

Keyword(s):

Differential Privacy ◽

Smart Metering

Download Full-text

Differential Privacy Approach for Big Data Privacy in Healthcare

Privacy and Security Policies in Big Data - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-2486-1.ch009 ◽

2017 ◽

pp. 191-213 ◽

Cited By ~ 1

Author(s):

Marmar Moussa ◽

Steven A. Demurjian

Keyword(s):

Big Data ◽

Data Sharing ◽

Data Privacy ◽

Large Scale ◽

Differential Privacy ◽

Security And Privacy ◽

Instrumental Case Study ◽

Large Scale Data ◽

Private Data

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.

Download Full-text