Information privacy

About: Information privacy is a research topic. Over the lifetime, 25412 publications have been published within this topic receiving 579611 citations. The topic is also known as: data privacy & data protection.

EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications

Abstract: The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.

The Role of Push-Pull Technology in Privacy Calculus: The Case of Location-Based Services

Abstract: Location-based services (LBS) use positioning technologies to provide individual users with reachability and accessibility that would otherwise not be available in the conventional commercial realm While LBS confer greater connectivity and personalization on consumers, they also threaten users' information privacy through granular tracking of their preferences, behaviors, and identity To address privacy concerns in the LBS context, this study extends the privacy calculus model to explore the role of information delivery mechanisms (pull and push) in the efficacy of three privacy intervention approaches (compensation, industry self-regulation, and government regulation) in influencing individual privacy decision making The research model was tested using data gathered from 528 respondents through a quasi-experimental survey method Structural equations modeling using partial least squares validated the instrument and the proposed model Results suggest that the effects of the three privacy intervention approaches on an individual's privacy calculus vary based on the type of information delivery mechanism (pull and push) Results suggest that providing financial compensation for push-based LBS is more important than it is for pull-based LBS Moreover, this study shows that privacy advocates and government legislators should not treat all types of LBS as undifferentiated but could instead specifically target certain types of services

On the privacy preserving properties of random data perturbation techniques

Abstract: Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. We question the utility of the random value distortion technique in privacy preservation. We note that random objects (particularly random matrices) have "predictable" structures in the spectral domain and it develops a random matrix-based spectral filtering technique to retrieve original data from the dataset distorted by adding random values. We present the theoretical foundation of this filtering method and extensive experimental results to demonstrate that in many cases random data distortion preserve very little data privacy. We also point out possible avenues for the development of new privacy-preserving data mining techniques like exploiting multiplicative and colored noise for preserving privacy in data mining applications.