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.

Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

Abstract: Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary’s prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.

Security and Privacy Issues of Fog Computing: A Survey

Abstract: Fog computing is a promising computing paradigm that extends cloud computing to the edge of networks. Similar to cloud computing but with distinct characteristics, fog computing faces new security and privacy challenges besides those inherited from cloud computing. In this paper, we have surveyed these challenges and corresponding solutions in a brief manner.

Pseudonym Changing at Social Spots: An Effective Strategy for Location Privacy in VANETs

Abstract: As a prime target of the quality of privacy in vehicular ad hoc networks (VANETs), location privacy is imperative for VANETs to fully flourish. Although frequent pseudonym changing provides a promising solution for location privacy in VANETs, if the pseudonyms are changed in an improper time or location, such a solution may become invalid. To cope with the issue, in this paper, we present an effective pseudonym changing at social spots (PCS) strategy to achieve the provable location privacy. In particular, we first introduce the social spots where several vehicles may gather, e.g., a road intersection when the traffic light turns red or a free parking lot near a shopping mall. By taking the anonymity set size as the location privacy metric, we then develop two anonymity set analytic models to quantitatively investigate the location privacy that is achieved by the PCS strategy. In addition, we use game-theoretic techniques to prove the feasibility of the PCS strategy in practice. Extensive performance evaluations are conducted to demonstrate that better location privacy can be achieved when a vehicle changes its pseudonyms at some highly social spots and that the proposed PCS strategy can assist vehicles to intelligently change their pseudonyms at the right moment and place.

Protecting Location Privacy Through Path Confusion

Abstract: We present a path perturbation algorithm which can maximize users’ location privacy given a quality of service constraint. This work concentrates on a class of applications that continuously collect location samples from a large group of users, where just removing user identifiers from all samples is insufficient because an adversary could use trajectory information to track paths and follow users’ footsteps home. The key idea underlying the perturbation algorithm is to cross paths in areas where at least two users meet. This increases the chances that an adversary would confuse the paths of different users. We first formulate this privacy problem as a constrained optimization problem and then develop heuristics for an efficient privacy algorithm. Using simulations with randomized movement models we verify that the algorithm improves privacy while minimizing the perturbation of location samples.

A Blockchain-Based Framework for Data Sharing With Fine-Grained Access Control in Decentralized Storage Systems

Abstract: In traditional cloud storage systems, attribute-based encryption (ABE) is regarded as an important technology for solving the problem of data privacy and fine-grained access control. However, in all ABE schemes, the private key generator has the ability to decrypt all data stored in the cloud server, which may bring serious problems such as key abuse and privacy data leakage. Meanwhile, the traditional cloud storage model runs in a centralized storage manner, so single point of failure may leads to the collapse of system. With the development of blockchain technology, decentralized storage mode has entered the public view. The decentralized storage approach can solve the problem of single point of failure in traditional cloud storage systems and enjoy a number of advantages over centralized storage, such as low price and high throughput. In this paper, we study the data storage and sharing scheme for decentralized storage systems and propose a framework that combines the decentralized storage system interplanetary file system, the Ethereum blockchain, and ABE technology. In this framework, the data owner has the ability to distribute secret key for data users and encrypt shared data by specifying access policy, and the scheme achieves fine-grained access control over data. At the same time, based on smart contract on the Ethereum blockchain, the keyword search function on the cipher text of the decentralized storage systems is implemented, which solves the problem that the cloud server may not return all of the results searched or return wrong results in the traditional cloud storage systems. Finally, we simulated the scheme in the Linux system and the Ethereum official test network Rinkeby, and the experimental results show that our scheme is feasible.