As smart grids are getting popular and being widely implemented, preserving the privacy of consumers is becoming more substantial. Power generation and pricing in smart grids depends on the continuously gathered information from the consumers. However, having access to the data relevant to the electricity consumption of each individual consumer is in conflict with its privacy. One common approach for preserving privacy is to aggregate data of different consumers and to use their smart-meters for calculating the bills. But in this approach, malicious consumers who send erroneous data to take advantage or disrupt smart grid cannot be identified. In this paper, we propose a new statistical-based scheme for data gathering and billing in which the privacy of consumers is preserved, and at the same time, if any consumer with erroneous data can be detected. Our simulation results verify these matters.
There are also some other researches on privacy-preserving data collection. For instance, in @cite_0 authors design a balanced anonymity and traceability for outsourcing small-scale linear data aggregation (called BAT-LA) in smart grid. Anonymity means that consumers’ identity should be kept secret and traceability means that imposter consumers should be traced. Here an important challenge is that many devices are not capable of handling required complicated computations. Hence, they have hired the idea of outsourcing computations with the help of public cloud. The paper utilizes elliptic curve cryptography and proxy re-encryption to make BAT-LA secure. BAT-LA is evaluated by comparing it with two other schemes, RVK @cite_11 , and LMO @cite_2 and it is shown that BAT-LA is more efficient in terms of confidentiality.
@cite_0 @cite_2 @cite_11
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Along with the development of information technology, the traditional electrical grid is moving to smart grid technology. By using the smart grid, the users and utility providers can more efficiently manage and generate power. Along with the advantages, the smart grid is also faced with new security concerns. In the smart grid, the user's citizen identity information should be preserved and the offensive user should be traced. For some low-capacity devices, it is indispensable to perform complicated computation by using outsourcing computation. The authors provide the outsourcing computation through public cloud. Anonymity and traceability are two important security properties in the smart grid. They are the unity of opposites. On the basis of the security requirements, they propose the balanced anonymity and traceability for outsourcing small-scale data linear aggregation (BAT-LA) in the smart grid. The formal definition, system model and security model are presented. Then, a concrete BAT-LA protocol is designed by using the elliptic curve cryptography and proxy re-encryption. Through security analysis and performance analysis, the designed BAT-LA protocol is provably secure and efficient. The ubiquity of mobile devices has brought forth the concept of participatory sensing, whereby ordinary citizens can now contribute and share information from the urban environment. However, such applications introduce a key research challenge: preserving the privacy of the individuals contributing data. In this paper, we study two different privacy concepts, k-anonymity and l-diversity, and demonstrate how their privacy models can be applied to protect users' spatial and temporal privacy in the context of participatory sensing. The first part of the paper focuses on schemes implementing k-anonymity. We propose the use of microaggregation, a technique used for facilitating disclosure control in databases, as an alternate to tessellation, which is the current state-of-the-art for location privacy in participatory sensing applications. We conduct a comparative study of the two techniques and demonstrate that each has its advantage in certain mutually exclusive situations. We then propose the Hybrid Variable size Maximum Distance to Average Vector (Hybrid-VMDAV) algorithm, which combines the positive aspects of microaggregation and tessellation. The second part of the paper addresses the limitations of the k-anonymity privacy model. We employ the principle of l-diversity and propose an l-diverse version of VMDAV (LD-VMDAV) as an improvement. In particular, LD-VMDAV is robust in situations where an adversary may have gained partial knowledge about certain attributes of the victim. We evaluate the performances of our proposed techniques using real-world traces. Our results show that Hybrid-VMDAV improves the percentage of positive identifications made by an application server by up to 100 and decreases the amount of information loss by about 40 . We empirically show that LD-VMDAV always outperforms its k-anonymity counterpart. In particular, it improves the ability of the applications to accurately interpret the anonymized location and time included in user reports. Our studies also confirm that perturbing the true locations of the users with random Gaussian noise can provide an extra layer of protection, while causing little impact on the application performance. Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the fc-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k - 1 other individuals. In this paper we study the problem of anonymity preserving data publishing in moving objects databases. We propose a novel concept of k-anonymity based on co-localization that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and positioning systems (e.g., GPS) imprecision, the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius delta represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,delta) -anonymity for moving objects databases. We first characterize the (k, delta)-anonymity problem and discuss techniques to solve it. Then we focus on the most promising technique by the point of view of information preservation, namely space translation. We develop a suitable measure of the information distortion introduced by space translation, and we prove that the problem of achieving (k,delta) -anonymity by space translation with minimum distortion is NP-hard. Faced with the hardness of our problem we propose a greedy algorithm based on clustering and enhanced with ad hoc pre-processing and outlier removal techniques. The resulting method, named NWA (Never Walk .Alone), is empirically evaluated in terms of data quality and efficiency. Data quality is assessed both by means of objective measures of information distortion, and by comparing the results of the same spatio-temporal range queries executed on the original database and on the (k, delta)-anonymized one. Experimental results show that for a wide range of values of delta and k, the relative error introduced is kept low, confirming that NWA produces high quality (k, delta)-anonymized data. Abstract With the proliferation of smart grids, traditional utilities are struggling to handle the increasing amount of metering data. Outsourcing the metering data to heterogeneous distributed systems has the potential to provide efficient data access and processing. In an untrusted heterogeneous distributed system environment, employing data encryption prior to outsourcing can be an effective way to preserve user privacy. However, how to efficiently query encrypted multidimensional metering data stored in an untrusted heterogeneous distributed system environment remains a research challenge. In this paper, we propose a high performance and privacy-preserving query (P2Q) scheme over encrypted multidimensional big metering data to address this challenge. In the proposed scheme, encrypted metering data are stored in the server of an untrusted heterogeneous distributed system environment. A Locality Sensitive Hashing (LSH) based similarity search approach is then used to realize the similarity query. To demonstrate utility of the proposed LSH-based search approach, we implement a prototype using MapReduce for the Hadoop distributed environment. More specifically, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-based Encryption (CP-ABE) policy is then used to control access to the search results. Therefore, only the requester with an authorized query attribute can obtain the correct secret keys to retrieve the metering data. We then prove that the P2Q scheme achieves data confidentiality and preserves the data owner’s privacy in a semi-trusted cloud. In addition, our evaluations demonstrate that the P2Q scheme can significantly reduce response time and provide high search efficiency without compromising on search quality (i.e. suitable for multidimensional big data search in heterogeneous distributed system, such as cloud storage system).