Additive Homomorphic Encryption Scheme Research Articles

RSDA: A Reliable and Secure Data Aggregation Scheme for Smart Grid

Smart grids are modern power distribution systems that utilize advanced technologies to optimize energy generation, distribution, and consumption. The huge amount of data generated by various sources, such as smart meters, sensors, and other devices, can be aggregated for efficient energy management, supporting demand response etc. However, this data can contain sensitive information about individual’s energy usage patterns, behaviour, and preferences, which raises several privacy concerns. Data reliability is another major issue, as the data send from the smart meters to the aggregators may get corrupted due to errors introduced by the communication channel. In this paper, a private-key additive homomorphic encryption scheme based on polar codes is proposed for privacy preserving data aggregation. The proposed scheme not only ensures data confidentiality but also provide data reliability. The security of this scheme relies on the difficulty in decoding the codeword and is achieved by embedding security in the generator matrix and error vector. Security analysis shows that the proposed scheme is resistant against all known attacks against private-key code-based cryptosystems. The performance analysis is evaluated in terms of error performance, communication overhead and computational complexity. The performance comparison with related additive homomorphic encryption scheme shows the efficiency of the proposed scheme.

Authenticable Data Analytics Over Encrypted Data in the Cloud

Statistical analytics on encrypted data requires a fully-homomorphic encryption (FHE) scheme. However, heavy computation overheads make FHE impractical. In this paper we propose a novel approach to achieve privacy-preserving statistical analysis on an encrypted database. The main idea of this work is to construct a privacy-preserving calculator to calculate attributes’ count values for later statistical analysis. To authenticate these encrypted count values, we adopt an authenticable additive homomorphic encryption scheme to construct the calculator. We formalize the notion of an authenticable privacy-preserving calculator that has properties of broadcasting and additive homomorphism. Further, we propose a cryptosystem based on binary vectors to achieve complex logic expressions for statistical analysis on encrypted data. With the aid of the proposed cryptographic calculator, we design several protocols for statistical analysis including conjunctive, disjunctive and complex logic expressions to achieve more complicated statistical functionalities. Experimental results show that the proposed scheme is feasible and practical.

Achieving Searchable Encryption Scheme With Search Pattern Hidden

Searchable Encryption (SE) enables a data owner to outsource encrypted data to an untrusted server while preserving the keyword search functionality. Typically, the server learns whether or not a query has been performed more than once, which is usually called the search pattern. However, such kind of information leakage might be leveraged to break query privacy. To further reduce such type of leakage and provide strong privacy guarantee, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a novel SE scheme based on the Paillier encryption scheme in INFOCOM’15. Unfortunately, their scheme cannot perform keyword search successfully, because the additive homomorphic property is not sufficient for their construction. In this article, we first show that why their scheme fails to return the correct search result, and then propose a new SE scheme by adopting a special additive homomorphic encryption scheme to achieve the multiplicative homomorphic property efficiently. Furthermore, we enhance the security on the user side. Specifically, we use random polynomials with an appropriate degree to guarantee that the user cannot learn anything other than the desired search result. Finally, we present a formal security analysis and implement our scheme on a real-world database, which demonstrates that our construction can achieve the desired security properties with good performance.

A secure channel code‐based scheme for privacy preserving data aggregation in wireless sensor networks

SummaryData aggregation is an efficient method to reduce the energy consumption in wireless sensor networks (WSNs). However, data aggregation schemes pose challenges in ensuring data privacy in WSN because traditional encryption schemes cannot support data aggregation. Homomorphic encryption schemes are promising techniques to provide end to end data privacy in WSN. Data reliability is another main issue in WSN due to the errors introduced by communication channels. In this paper, a symmetric additive homomorphic encryption scheme based on Rao‐Nam scheme is proposed to provide data confidentiality during aggregation in WSN. This scheme also possess the capability to correct errors present in the aggregated data. The required security levels can be achieved in the proposed scheme through channel decoding problem by embedding security in encoding matrix and error vector. The error vectors are carefully designed so that the randomness properties are preserved while homomorphically combining the data from different sensor nodes. Extensive cryptanalysis shows that the proposed scheme is secure against all attacks reported against private‐key encryption schemes based on error correcting codes. The performance of the encryption scheme is compared with the related schemes, and the results show that the proposed encryption scheme outperforms the existing schemes.

Design of additive homomorphic encryption with multiple message spaces for secure and practical storage services over encrypted data

Homomorphic encryption scheme is one of the useful tools for handling encrypted information in storage services. However, most of existing schemes have not been used in practical applications due to their inefficiency or lack of functionalities. For example, fully homomorphic encryption, which can provide arbitrary operations, is inefficient. On the other hand, other homomorphic encryptions, including additive homomorphic encryptions, which are efficient enough to be used in practice, are limited in operations for practical applications. In this paper, we devise a message-encoding technique which can split a message space of an additive homomorphic encryption into multiple sub-spaces for different messages. Our technique guarantees that we can deal with multiple sets of data (each data set is assigned to each sub-space), so that multiple data can be computed at once in an additive manner. Using our new technique, we construct two additive homomorphic encryptions with valuable properties. The first one is an additive homomorphic encryption scheme which can be used for evaluating some statistical information, such as the mean and the variance. To give a concrete scheme, we apply our technique to Paillier’s scheme which supports the additive homomorphism. Note that our scheme is the first additive homomorphic encryption which supports the evaluation of both the mean and the variance of encrypted data. We also give two modifications of the first application to improve its practicality. As the second application of our technique, we propose an additive homomorphic encryption scheme which can support the functionality of error detection in homomorphic operations. The second scheme is also designed based on Paillier’s scheme. Note that one of the remarkable advantages of our technique is that it can be applied to any additive homomorphic encryption for supporting the above-mentioned functionalities.

Privacy-enhanced distance computation with applications

Location privacy has been regarded as an important requirement for location-based service on mobile devices such as mobile phones, where location information might have to be protected against unauthorised or even authorised curious-but-honest parties in some cases. We propose a scheme, which provides a method for a location server to identify the nearest mobile object amongst n mobile objects to a target mobile object without revealing the location of any participant to the location server. We describe our protocol by using a practical application, where the aim is to identify a closest service vehicle among registered service vehicles to the target car which requires a service, while the locations of all participants are protected against the location server. Our scheme only requires an additive homomorphic encryption scheme without the need of fully homomorphic encryption as required by all other related schemes on location privacy.

A Secure Data Aggregation Scheme Based on Appropriate Cryptographic Primitives in Heterogeneous Wireless Sensor Networks

Energy cost of transmitting a single bit of information is approximately the same as that needed for processing a thousand operations in a typical sensor node. Thus, a practical way to prolong a wireless sensor network lifetime is to reduce the sensor energy consumption in data transmissions. Data aggregation is an efficient way to minimize energy consumption on sensors. In this paper, we propose a practical secure data aggregation scheme, Sen-SDA, based on an additive homomorphic encryption scheme, an identity-based signature scheme, and a batch verification technique with an algorithm for filtering injected false data. We then investigate the feasibility of our scheme using low-cost microcontrollers choosing two popular IEEE 802.15.4-compliant wireless sensor network hardware platforms, MICAz and Tmote Sky, used in real-life deployments.

Privacy-Preserving and Efficient Friend Recommendation in Online Social Networks

The popularity of online social networks (OSNs) is on constant rise due to various advantages, including online communication and sharing information of interest among friends. It is often that users want to make new friends to expand their social connections as well as to obtain information from a broad range of people. Friend recommendation is a very important application in many OSNs and has been studied extensively in the recent past. However,with the growing concerns about user privacy, there is a strong need to develop privacy-preserving friend recommendation methods for social networks. In this paper, we propose two novel methods to recommend friends for a given user by using the common neighbors proximity measure in a privacy-preserving manner. The first method is based on the properties of an additive homomorphic encryption scheme and also utilizes a universal hash function for efficiency purpose. The second method utilizes the concept of protecting the source privacy through anonymousmessage routing and recommends friends accurately and efficiently. In addition, we empirically compare the efficiency and accuracy of the proposed protocols, and address the implementation details of the two methods in practice. The proposed protocols provide a trade-off among security, accuracy, and efficiency; thus, users or the network provider can choose between these two protocols depending on the underlying requirements.

A Cryptographic Solution to Privacy-Preserving Two-Party Sign Test Computation on Vertically Partitioned Data

Statistical hypothesis test is an important data analysis theory that has found applications in various research fields. It provides a theoretical foundation to determine whether sufficient evidence exists to reject hypothesis for the trial using experimental results, in order to make a decision. In this paper, we address one of the fundamental test theories: the Nonparametric Sign Test, under the privacy-preserving context. In this context, two parties, Alice and Bob, would like to perform a sign test on their joint dataset, but neither of them is willing to disclose their private raw data to the other party. More specifically, this paper addresses the problem where the joint dataset consists of two vertically partitioned datasets. Our previous work [14] has addressed this problem using data perturbation techniques. However, in a case when the privacy of individual data objects and data subjects are of high concern, using data perturbation techniques may not be sufficient. This paper proposes an alternative solution to this problem by employing an additive homomorphic encryption scheme and an on-line STTP. We show that this solution can offer better privacy preservation, in terms of individual data confidentiality and individual privacy, than the previous solution. A security comparison with TTPV and P22NSTV solutions is also presented.