No Arabic abstract
In order to receive personalized services, individuals share their personal data with a wide range of service providers, hoping that their data will remain confidential. Thus, in case of an unauthorized distribution of their personal data by these service providers (or in case of a data breach) data owners want to identify the source of such data leakage. Digital fingerprinting schemes have been developed to embed a hidden and unique fingerprint into shared digital content, especially multimedia, to provide such liability guarantees. However, existing techniques utilize the high redundancy in the content, which is typically not included in personal data. In this work, we propose a probabilistic fingerprinting scheme that efficiently generates the fingerprint by considering a fingerprinting probability (to keep the data utility high) and publicly known inherent correlations between data points. To improve the robustness of the proposed scheme against colluding malicious service providers, we also utilize the Boneh-Shaw fingerprinting codes as a part of the proposed scheme. Furthermore, observing similarities between privacy-preserving data sharing techniques (that add controlled noise to the shared data) and the proposed fingerprinting scheme, we make a first attempt to develop a data sharing scheme that provides both privacy and fingerprint robustness at the same time. We experimentally show that fingerprint robustness and privacy have conflicting objectives and we propose a hybrid approach to control such a trade-off with a design parameter. Using the proposed hybrid approach, we show that individuals can improve their level of privacy by slightly compromising from the fingerprint robustness. We implement and evaluate the performance of the proposed scheme on real genomic data. Our experimental results show the efficiency and robustness of the proposed scheme.
This paper presents a novel fingerprinting scheme for the Intellectual Property (IP) protection of Generative Adversarial Networks (GANs). Prior solutions for classification models adopt adversarial examples as the fingerprints, which can raise stealthiness and robustness problems when they are applied to the GAN models. Our scheme constructs a composite deep learning model from the target GAN and a classifier. Then we generate stealthy fingerprint samples from this composite model, and register them to the classifier for effective ownership verification. This scheme inspires three concrete methodologies to practically protect the modern GAN models. Theoretical analysis proves that these methods can satisfy different security requirements necessary for IP protection. We also conduct extensive experiments to show that our solutions outperform existing strategies in terms of stealthiness, functionality-preserving and unremovability.
Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26% more fingerprinting scripts than the state-of-the-art. We show that an API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10% of the top-100K websites and over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.
Energy storage units (ESUs) including EVs and home batteries enable several attractive features of the modern smart grids such as effective demand response and reduced electric bills. However, uncoordinated charging of ESUs stresses the power system. In this paper, we propose privacy-preserving and collusion-resistant charging coordination centralized and decentralized schemes for the smart grid. The centralized scheme is used in case of robust communication infrastructure that connects the ESUs to the utility, while the decentralized scheme is useful in case of infrastructure not available or costly. In the centralized scheme, each energy storage unit should acquire anonymous tokens from a charging controller (CC) to send multiple charging requests to the CC via the aggregator. CC can use the charging requests to enough data to run the charging coordination scheme, but it cannot link the data to particular ESUs or reveal any private information. Our centralized scheme uses a modified knapsack problem formulation technique to maximize the amount of power delivered to the ESUs before the charging requests expire without exceeding the available maximum charging capacity. In the decentralized scheme, several ESUs run the scheme in a distributed way with no need to aggregator or CC. One ESU is selected as a head node that should decrypt the ciphertext of the aggregated messages of the ESUs messages and broadcast it to the community while not revealing the ESUs individual charging demands. Then, ESUs can coordinate charging requests based on the aggregated charging demand while not exceeding the maximum charging capacity. Extensive experiments and simulations are conducted to demonstrate that our schemes are efficient and secure against various attacks, and can preserve ESU owners privacy.
In this article, we propose a new construction of probabilistic collusion-secure fingerprint codes against up to three pirates and give a theoretical security evaluation. Our pirate tracing algorithm combines a scoring method analogous to Tardos codes (J. ACM, 2008) with an extension of parent search techniques of some preceding 2-secure codes. Numerical examples show that our code lengths are significantly shorter than (about 30% to 40% of) the shortest known c-secure codes by Nuida et al. (Des. Codes Cryptogr., 2009) with c = 3. Some preliminary proposal for improving efficiency of our tracing algorithm is also given.
With the support of cloud computing, large quantities of data collected from various WSN applications can be managed efficiently. However, maintaining data security and efficiency of data processing in cloud-WSN (C-WSN) are important and challenging issues. In this paper, we present an efficient data outsourcing scheme based on CP-ABE, which can not only guarantee secure data access, but also reduce overall data processing time. In our proposed scheme, a large file is divided into several data blocks by data owner (DO) firstly. Then, the data blocks are encrypted and transferred to the cloud server in parallel. For data receiver (DR), data decryption and data transmission is also processed in parallel. In addition, data integrity can be checked by DR without any master key components. The security analysis shows that the proposed scheme can meet the security requirement of C-WSN. By performance evaluation, it shows that our scheme can dramatically improve data processing efficiency compared to the traditional CP-ABE method.