A privacy-preserving aggregation scheme based on immunological negative surveys for smart meters

Highlights

• We analyze the privacy-preserving ability of the traditional negative survey when it is used to collect time-series data, and demonstrate that traditional negative survey might disclose the privacy of users’ time-series data.

• We propose an improved strategy on the negative survey that can be used to collect the time-series data and then apply the improved negative survey to collect the power consumption data.

Abstract

The smart meter is a basic device of the smart grid, which improves the efficiency of the power grid and brings a lot of convenience for the industry and people’s daily life. However, real-time power consumption data contain some sensitive information, and could disclose the privacy of users. As an immunological technique, the negative survey is proposed to preserve the privacy of static data. In this paper, firstly, we demonstrate that traditional negative survey might disclose the privacy of users when it is used to collect time-series data. Secondly, we propose an improved negative survey method for collecting the time-series data. Thirdly, for the first time, we apply the negative survey to preserving the privacy of the power consumption data aggregated from smart meters. Theoretical analysis and experimental results demonstrate that the method proposed in this paper could aggregate the power consumption data while preserving the privacy of users. Compared with existing techniques, our method is simple and efficient, and does not need a trusted third party. Moreover, it could tolerate the failure of some users and resist differential attack.

Introduction

Electricity is a main form of energy, which is also important for the development of industry. As the next generation of power grid, the smart grid has received a lot of attention. The smart meter is one of the basic pieces of equipment for smart grid data collection. It is also the basis of many functions of the smart grid. It plays an important role in load monitoring, charging electricity fee, detecting user behavior patterns, improving electricity efficiency, detecting theft of electricity, anomaly detection and so on [1], [2], [3].

As an important task, load monitoring could improve the efficiency and the security of the power grid, which needs the sum of all user’s power consumption in one area. However, as mentioned in [4], [5], the power consumption data obtained by the smart meter are so fine-grained that an adversary could infer some sensitive information along with the users’ power consumption, such as the customs in daily life, the number of persons in the household, even the information of domestic appliances currently used. Then, the adversary could conduct spam harassment and advertising campaigns according to the sensitive information he obtains. This prospect arouses users’ concern about the privacy of their power consumption data [6]. Although many policies and laws have been proposed to preserve the privacy of power consumption data, users still worry about their privacy. Therefore, it is very important to develop a privacy-preserving aggregation scheme for smart meters. And many researches have been performed to preserve the users’ privacy when smart meters aggregate information from users. There are three primary types of schemes to preserve the privacy when the smart meters aggregate power consumption data from users: the anonymization-based aggregation schemes, the perturbation-based aggregation schemes and the encryption-based aggregation schemes [7]. For the anonymization-based aggregation schemes, they usually require a thrusted third party. For the perturbation-based aggregation schemes, although they are simple and efficient, some of them could not withstand individual failures. And for the encryption-based aggregation schemes, they are time-consuming and could not defend differential attack. Moreover, in order to ensure the security, they usually increase the size of the ciphertext [8]. The details of typical schemes will be reviewed in Section 2.2.

Inspired by human immunological mechanisms, Artificial Immune System was proposed and widely used in optimization [9], computer security [10], data privacy [11] and so on. As a branch of artificial immune system, the negative survey [12] was proposed to collect sensitive information. It could collect the sensitive information while preserving individual privacy. Because it is simple and could preserve privacy without the trusted third party, so the negative survey was used to collect many different kinds of sensitive information [11]. In the negative survey, the participant is just asked to select a category that he does not belong to (called negative category), rather that the category he belongs to (called positive category). And the collector could infer the distribution of positive categories from the distribution of negative categories by some statistical methods (called reconstruction methods), such as NStoPS [12], NStoPS-I [13], and NStoPS-II [13]. However, the traditional negative survey [12] is mainly applied to collecting static data. It does not consider the privacy threat caused by time-series data such as power consumption data.

In this paper, we propose a method based on negative survey for collecting the time-series data from users, and employ it to collect power consumption data. It could provide the aggregated data for the smart grid while preserving the privacy of individual power consumption data. The power consumption is a kind of time-series data. The power consumption will increase/decrease as domestic appliances are turned on/off. Thus, the changes of power consumption highly depend on the users’ behaviors. Our method does not limit the time frame of the time series data, which can work continually. The purpose of our method is to preserve the privacy of users’ power consumption when monitoring the load. The privacy preserved in this paper is the value of user’s power consumption. Because when it is disclosed, the adversary could infer some sensitive information of users, and conduct harassment. For example, the adversary could conduct spam harassment and advertising campaigns according to the information of user’s domestic appliances.

Our contributions in this paper can be summarized as follows.

• We analyze the privacy-preserving ability of the traditional negative survey when it is used to collect time-series data, and demonstrate that traditional negative survey might disclose the privacy of users’ time-series data.

• We propose an improved strategy on the negative survey that can be used to collect the time-series data and then apply the improved negative survey to collect the power consumption data. The theoretical analysis demonstrates that our method could preserve the privacy of user’s power consumption data. Besides, the proposed method does not require a trusted third party and could resist differential attacks. Moreover, it is robust, simple and efficient.

• The experimental results conducted on synthetic and real dataset demonstrate that our method could provide the sum of power consumption data to the smart grid for load monitoring.

The rest of this paper is organized as follows. Some related work is introduced in Section 2. The system model used in this paper is described in Section 3. Section 4 analyzes the privacy of traditional negative survey when it collects time-series data. The details of our method are given in Section 5. The privacy analysis is shown in Section 6. The experimental results and corresponding analysis are given in Section 7. In Section 8, we discuss proposed method. Section 9 gives the conclusion and future work.