Unauthorized Redistribution Research Articles

Robust Fingerprint of Location Trajectories Under Differential Privacy.

Location-based services have brought significant convenience to people in their daily lives, and the collected location data are also in high demand. However, directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To address this, we propose a novel fingerprinting scheme that simultaneously identifies unauthorized redistribution of location datasets and provides differential privacy guarantees for the shared data. Observing data utility degradation due to differentially-private mechanisms, we introduce a utility-focused post-processing scheme to regain spatiotemporal correlations between points in a location trajectory. We further integrate this post-processing scheme into our fingerprinting scheme as a sampling method. The proposed fingerprinting scheme alleviates the degradation in the utility of the shared dataset due to the noise introduced by differentially-private mechanisms (i.e., adds the fingerprint by preserving the publicly known statistics of the data). Meanwhile, it does not violate differential privacy throughout the entire process due to immunity to post-processing, a fundamental property of differential privacy. Our proposed fingerprinting scheme is robust against known and well-studied attacks against a fingerprinting scheme including random flipping attacks, correlation-based flipping attacks, and collusions among multiple parties, which makes it hard for the attackers to infer the fingerprint codes and avoid accusation. Via experiments on two real-life location datasets and two synthetic ones, we show that our scheme achieves high fingerprinting robustness and outperforms existing approaches. Besides, the proposed fingerprinting scheme increases data utility for differentially-private datasets, which is beneficial for data analyzers.

Privacy-Preserving Database Fingerprinting.

When sharing relational databases with other parties, in addition to providing high quality (utility) database to the recipients, a database owner also aims to have (i) privacy guarantees for the data entries and (ii) liability guarantees (via fingerprinting) in case of unauthorized redistribution. However, (i) and (ii) are orthogonal objectives, because when sharing a database with multiple recipients, privacy via data sanitization requires adding noise once (and sharing the same noisy version with all recipients), whereas liability via unique fingerprint insertion requires adding different noises to each shared copy to distinguish all recipients. Although achieving (i) and (ii) together is possible in a naïve way (e.g., either differentially-private database perturbation or synthesis followed by fingerprinting), this approach results in significant degradation in the utility of shared databases. In this paper, we achieve privacy and liability guarantees simultaneously by proposing a novel entry-level differentially-private (DP) fingerprinting mechanism for relational databases without causing large utility degradation. The proposed mechanism fulfills the privacy and liability requirements by leveraging the randomization nature of fingerprinting and transforming it into provable privacy guarantees. Specifically, we devise a bit-level random response scheme to achieve differential privacy guarantee for arbitrary data entries when sharing the entire database, and then, based on this, we develop an -entry-level DP fingerprinting mechanism. We theoretically analyze the connections between privacy, fingerprint robustness, and database utility by deriving closed form expressions. We also propose a sparse vector technique-based solution to control the cumulative privacy loss when fingerprinted copies of a database are shared with multiple recipients. We experimentally show that our mechanism achieves strong fingerprint robustness (e.g., the fingerprint cannot be compromised even if the malicious database recipient modifies/distorts more than half of the entries in its received fingerprinted copy), and higher database utility compared to various baseline methods (e.g., application-dependent database utility of the shared database achieved by the proposed mechanism is higher than that of the considered baselines).

Robust fingerprinting of genomic databases.

MotivationDatabase fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel’s law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks.ResultsVia experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP–phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP–phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy.Availability and implementation https://github.com/xiutianxi/robust-genomic-fp-github.

Towards differential access control and privacy-preserving for secure media data sharing in the cloud

The development of cloud computing techniques has promoted the continuous sharing of a tremendous amount of media data. However, the involved parties in such processes usually share different or even conflicting benefits, causing non-orthogonal security concerns. The data owners worry about their private data being abused through illegal access or unauthorized redistribution by the requesters, while the latter ones are vulnerable to being profiled (e.g., interests, age) through their data access histories. Since most existing approaches address these problems individually, the security requirements of different roles cannot be properly satisfied. In this paper, we attempt to provide a joint effort that flexibly addresses the above security concerns. We first design a new cryptographic primitive (i.e., TFPRE-OT) by exploiting type-based proxy re-encryption for fine-grained sharing and oblivious transfer for hiding the access histories of data requesters. Using TFPRE-OT as the building block, a novel data sharing scheme is proposed to achieve differential access control and protect privacy disclosure in general cloud computing settings. We provide a thorough security analysis, showing that the proposed method can fulfill all desired security requirements. Finally, we validate the effectiveness of our design with both case studies and extensive experiments.

Reversible color video watermarking scheme based on hybrid of integer-to-integer wavelet transform and Arnold transform

<p>Unauthorized redistribution and illegal copying of digital contents are serious issues which have affected numerous types of digital contents such as digital video. One of the methods, which have been suggested to support copyright protection, is to hide digital watermark within the digital video. This paper introduces a new video watermarking system which based on a combination of Arnold transform and integer wavelet transforms (IWT). IWT is employed to decompose the cover video frames whereby Arnold transform is used to scramble the watermark which is a grey scale image. Scrambling the watermark before the concealment makes the transmission more secure by disordering the information. The system performance was benchmarked against related video watermarking schemes, in which the evaluation processes consist of testing against several video operations and attacks. Consequently, the scheme has been demonstrated to be perfectly robust.</p>

Joint fingerprinting and decryption method for color images based on quaternion rotation with cipher quaternion chaining

This paper addresses the problem of unauthorized redistribution of multimedia content by malicious users (pirates). In this method three color channels of the image are considered a 3D space and each component of the image is represented as a point in this 3D space. The distribution side uses a symmetric cipher to encrypt perceptually essential components of the image with the encryption key and then sends the encrypted data via multicast transmission to all users. The encryption involves rotation, and translation of points in 3D color space using quaternion algebra. Each user has a unique decryption key which is different from the encryption key. The differences between the common encryption key and the individual user’s decryption key cause the decrypted image to contain minor changes which are user’s fingerprint. A computer-based simulation was conducted to examine the method’s robustness against noise, compression, and collusion attacks.

DWT and Lifting Wavelet Transform Based Robust Watermarking for Color Image

Illegal copying and unauthorized redistribution of digital media is a serious problem which has affected various forms of digital contents including digital images. One of the means which has been proposed to provide support for copyright protection is to embed digital watermarking within the digital images. However, the effectiveness of the watermarks themselves are affected by various types of attacks and one of the most serious challenges is perhaps image compressions whereby researchers are forced to develop robust watermarking systems which should survive various forms of compression techniques since digital images are usually compressed before they are loaded on the Internet. In this paper, a new robust image watermarking algorithm based on two transforms has been proposed whereby both watermark and cover images are in colored forms. Two wavelet transforms of different nature have been applied, one for each watermark and cover image. These transforms are Discrete wavelet transform (DWT) and a second generation of wavelet transform; lifting wavelet transform (LWT). The performance of the scheme has been benchmarked against similar watermarking techniques and it outperformed them, whereby the evaluation protocols include testing against various image modifications and attacks and subsequently the system has been proven to be quite robust.

Local and global structure preserving hashing for fast digital fingerprint tracing

Digital fingerprinting is a promising approach to protect multimedia contents from unauthorized redistribution. Whereas, large scale and high dimensionality make existing fingerprint detection meth...

Experimental Study of Improved Watermarking Scheme on Relational Databases

The main objective of watermarking is to protect a digital content from unauthorized redistribution and copying by enabling ownership provability over the content. The goal of digital watermarking is to insert a robust and imperceptible watermark into the digital content such that the mark does not destroy the value of the content, and the mark is hard to be removed by attackers without destroying the utility of content. The measurement of the value of the content is closely related to the data type and its intended use. We have proposed a new marking scheme called as improved watermarking. Watermarking scheme first generates the bit string of fixed length. The mark bit is computed with the help of watermark bit and mask bit, which is used to mark the certain bit location of the attribute value. General Terms Relational Database Security, Improved Watermarking on Relational Database

Matrix-based robust joint fingerprinting and decryption method for multicast distribution of multimedia

This paper addresses the problem of unauthorized redistribution of multimedia content by malicious users (pirates). The solution proposed here is a new joint fingerprinting and decryption method which meets the requirements for both imperceptibility and robustness of fingerprints and scalability in terms of design and distribution of fingerprinted multimedia content. The proposed method uses a simple block cipher based on matrix multiplication to encrypt images which are then sent to all users via multicast transmission. Individual decryption keys are designed depending on the users’ fingerprints so that a different fingerprint will be introduced into the image during decryption for each unique decryption key. Encryption and fingerprinting are performed on DCT coefficients, which results in high robustness against compression, low-pass filtering, scaling, etc. The experimental results show that the proposed method is robust against collusion attacks and compression.

Kernelized Neighborhood Preserving Hashing for Social-Network-Oriented Digital Fingerprints

Digital fingerprinting is a promising approach to protect multimedia content from unauthorized redistribution. However, the existing fingerprints are unsuitable for social network tasks, because they fail to represent the social network structure, which incurs inefficient fingerprint coding. In addition, they are infeasible to efficiently trace colluders due to the large scale of social networks. To address these problems, we design a novel fingerprint, which consists of community relationship code and user identification code. Aiming to preserving the social network structure, we propose a kernelized neighborhood preserving hashing method to generate community relationship codes. The proposed method assigns similar community relationship codes to users in the same or close communities, which improves the anticollusion performance. Because the community relationship codes are binary and neighborhood preserving, they can be used for fast indexing and retrieval. To accelerate the collusion fingerprint tracing, we treat the community relationship codes as index keys to construct a hash table and an inverted index table. Based on the tables, we correspondingly propose an efficient fingerprint detection method. Extensive experiments show that the proposed fingerprint is suitable for social network tasks and the real colluders can be efficiently identified by the proposed fingerprint detection approach.

Local and global structure preserving hashing for fast digital fingerprint tracing

Digital fingerprinting is a promising approach to protect multimedia contents from unauthorized redistribution. Whereas, large scale and high dimensionality make existing fingerprint detection methods fail to trace the traitors efficiently. To handle this problem, we propose a novel local and global structure preserving hashing to conduct fast fingerprint detection. This is the first work that introduces hash-based similarity search method to perform fingerprint detection. Applying the hashing method, we obtain a neighborhood-preserving low-dimensional representation (e. g. hash code) for each fingerprint. Through hash codes, we can find the nearest neighbors of the extracted fingerprint, thereby tracing the real traitors within a small range. Preserving the local structure facilitates to find the nearest neighbors of the query fingerprint efficiently, and preserving the global structure ensures hash codes of fingerprints as discriminative as possible. These properties make the proposed approach efficient to trace the real traitors. Extensive experiments demonstrate that the proposed approach outperforms traditional linear scan detection methods in term of efficiency.

A new fingerprinting scheme using social network analysis for majority attack

Digital fingerprinting is a technology to protect multimedia content from unauthorized redistribution. However, collusion attack is a cost-efficient attack for digital fingerprinting, where groups of dishonest users create a pirate copy using their copies for the purpose of attenuating or removing the fingerprints. In this paper, FCBSN, Fingerprinting Code Based on Social Networks, for coding the user's fingerprints to resist majority attack, is proposed. The proposed scheme stems from the concept of coalition which always occurred in a social network. Different from all existing work, we explore the notion of the hierarchical community structure of social network and its intrinsic properties to assign fingerprints to users, drawing on the social relation according to the similar metric between two users. Theoretical analysis and experimental results show that the FCBSN detector outperforms the existing group detector for BS code and Tardos detector by large margins.

False Negative probabilities in Tardos codes

Forensic watermarking is the application of digital watermarks for the purpose of tracing unauthorized redistribution of content. One of the most powerful types of attack on watermarks is the collusion attack, in which multiple users compare their differently watermarked versions of the same content. Collusion-resistant codes have been developed against these attacks. One of the most famous such codes is the Tardos code. It has the asymptotically optimal property that it can resist \(c\) attackers with a code of length proportional to \(c^2\). Determining error rates for the Tardos code and its various extensions and generalizations turns out to be a nontrivial problem. In recent work we developed an approach called the convolution and series expansion (CSE) method to accurately compute false positive accusation probabilities. In this paper we extend the CSE method in order to make it possible to compute a bound on the False Negative accusation probabilities.

Game-Theoretic Pricing for Video Streaming in Mobile Networks

Mobile phones are among the most popular consumer devices, and the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing data plans from service providers. Due to the ubiquity of mobile phones and phone-to-phone communication technologies, data-plan subscribers can redistribute the video content to nonsubscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized redistribution behavior to protect or maximize his/her own profit. In this paper, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content-redistribution network. We model the behavior between the subscribers and the secondary buyers as a noncooperative game and find the optimal price and quantity for both groups of users. Based on the behavior of users in the redistribution network, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.

Risk-Distortion Analysis for Video Collusion Attacks: A Mouse-and-Cat Game

Copyright protection is a key issue for video sharing over public networks. To protect the video content from unauthorized redistribution, digital fingerprinting is commonly used. To develop an efficient collusion-resistant fingerprinting scheme, it is very important for the system designer to understand how the behavior dynamics of colluders affect the performance of collusion attack. In the literature, little effort has been made to explicitly study the relationship between risk, e.g., the probability of the colluders to be detected, and the distortion of the colluded signal. In this paper, we investigate the risk-distortion relationship for the linear video collusion attack with Gaussian fingerprint. We formulate the optimal linear collusion attack as an optimization problem of finding the optimal collusion parameters to minimize the distortion subject to a risk constraint. By varying the risk constraint and solving the corresponding optimization problem, we can derive the optimal risk-distortion curve. Moreover, based upon the observation that the detector/attacker can each improve the detection/attack performance with the knowledge of his/her opponent's strategy, we formulate the attack and detection problem as a dynamic mouse and cat game and study the optimal strategies for both the attacker and detector. We show that if the detector uses a fixed detection strategy, the attacker can estimate the detector's strategy and choose the corresponding optimal strategy to attack the fingerprinted video with a small distortion. However, if the detector is powerful, i.e., the detector can always estimate the attacker's strategy, the best strategy for the attacker is the min-max strategy. Finally, we conduct several experiments to verify the proposed risk-distortion model using real video data.

Fingerprinting Compressed Multimedia Signals

Digital fingerprinting is a technique to deter unauthorized redistribution of multimedia content by embedding a unique identifying signal in each legally distributed copy. The embedded fingerprint can later be extracted and used to trace the originator of an unauthorized copy. A group of users may collude and attempt to create a version of the content that cannot be traced back to any of them. As multimedia data is commonly stored in compressed form, this paper addresses the problem of fingerprinting compressed signals. Analysis is carried out to show that due to the quantized nature of the host signal and the embedded fingerprint, directly extending traditional fingerprinting techniques for uncompressed signals to the compressed case leads to low collusion resistance. To overcome this problem and improve the collusion resistance, a new technique for fingerprinting compressed signals called Anti-Collusion Dither (ACD) is proposed, whereby a random dither signal is added to the compressed host before embedding so as to make the effective host signal appear more continuous. The proposed technique is shown to reduce the accuracy with which attackers can estimate the host signal, and from an information theoretic perspective, the proposed ACD technique increases the maximum number of users that can be supported by the fingerprinting system under a given attack. Both analytical and experimental studies confirm that the proposed technique increases the probability of identifying a guilty user and can approximately quadruple the collusion resistance compared to conventional Gaussian fingerprinting.

Decode-Time Forensic Watermarking of AAC Bitstreams

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In digital rights-management systems, forensic watermarking complements encryption and deters the capture and unauthorized redistribution of the rendered content. In this paper, we propose a novel watermarking method which is integrated into the advanced audio coding (AAC) standard's decoding process. For predefined frequency bands, the method intercepts and modifies the scale factors, which are utilized for dequantization of spectral coefficients. It thereby modulates the short-time envelope of the bandlimited audio and embeds a watermark which is robust to various attacks, such as capture with a microphone and recompression at lower bit rates. Inclusion of watermark embedding in the AAC decoder has practically no effect on the decoding complexity. As a result, the proposed method can be integrated even into resource-constrained devices, such as portable players without any additional hardware. </para>

Symmetric Tardos fingerprinting codes for arbitrary alphabet sizes

Fingerprinting provides a means of tracing unauthorized redistribution of digital data by individually marking each authorized copy with a personalized serial number. In order to prevent a group of...

Multicast Copyright Protection System Based on a Trusted Soft-Engine

One of the main problems that remains to be solved in pay-per-view Internet services is copyright protection. In Internet services such as near video on-demand, where multicast is used, watermarking can be directly applied. On the other hand, multicast fingerprinting has been seldom studied because delivering different marked content for different receivers seems a contradiction with multicast basics. In this paper we present a solution to prevent unauthorized redistribution of content in multicast scenarios. The system is based on a trusted soft-engine embedded in the receiver and co-managed by the content distributor. The trusted soft-engine is responsible for the client-side multicast key management functions. It only will allow the decryption and displaying of the actual data if it has previously inserted a fingerprinting mark with the identity of the decoder.