Localization systems are wildly used in the applications which need the accurate location of tracking objects. In this paper, we use Ubisense sensors and corresponding proprietary software to create such a system, and adjust it to fit into the outdoor playground environment where the monitored targets are the toddler-age children. After the system is implemented, we collect the raw location data of each tag from Ubisense software and import them into our self-developed program for storage. Practical test and theoretical analysis of tracked location data are conducted after the system is configured. We conclude that our system can better help the educator design the layout of the playground, determine the sub-zone occupied with most children, and investigate the children’s preference activities or toys.

Secure multi-party computation (MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. Among many secure MPC primitives, Shamir's secret sharing (SSS) has the advantages of having low complexity and information-theoretic security. However, SSS requires multiple honest participants and is susceptible to collusion attacks. In this paper, we provide a detailed analysis of different types of collusion attacks and propose novel mechanisms to deter such attacks in a fully distributed manner. Focusing on outsourced computing environments where secret data owners can collaborate on a public computing platform, we study collusion attacks using game theory. For those attacks where the thefts are detectable, we show that they can be effectively deterred by an explicit retaliation mechanism between data owners. The result is based on a comprehensive analysis that takes into account the cost of collusion, the privacy preference, and the associated uncertainty. For those attacks where the thefts cannot be detected, we expand the analysis to include the computing platform and provide deterrence through deceptive collusion requests as well as a novel cryptographic censorship protocol. The correctness and the privacy of the protocols are proved under the rational adversarial model. Our SSS-based protocols are shown to outperform the state-of-the-art garbled circuit systems, while our simulation results validate the proposed mechanism designs in deterring collusion.

Many secure multiparty computation (SMC) protocols use Shamir's Secret Sharing (SSS) scheme as a building block. Unlike other cryptographic SMC techniques such as garbled circuits (GC), SSS requires no data expansion and achieves information theoretic security. A weakness of SSS is the possibility of collusion attacks from participants. In this paper, we propose an evolutionary game-theoretic (EGT) approach to deter collusion in SSS-based protocols. First, we consider the possibility of detecting the leak of secret data caused by collusion, devise an explicit retaliation mechanism, and show that the evolutionary stable strategy of this game is not to collude if the technology to detect the leakage of secret is readily available. Then, we consider the situation in which data-owners are unaware of the leakage and thereby unable to retaliate. Such behaviors are deterred by injecting occasional fake collusion requests, and detected by a censorship scheme that destroys subliminal communication. Comparison results show that our collusion-deterred SSS system significantly outperforms GC, while game simulations confirm the validity of our EGT framework on modeling collusion behaviors.

In the recent years, color and depth camera systems have attracted intensive attention because of its wide applications in image-based rendering, 3D model reconstruction, and human tracking and pose estimation. These applications often require multiple color and depth cameras to be placed with wide separation so as to capture the scene objects from different prospectives. The difference in modality and the wide baseline make calibration a challenging problem. In this paper, we present an algorithm that simultaneously and automatically calibrates the extrinsics across multiple color and depth cameras across the network. Rather than using the standard checkerboard, we use a sphere as a calibration object to identify the correspondences across different views. We experimentally demonstrate that our calibration framework can seamlessly integrate different views with wide baselines that outperforms other techniques in the literature.

Biometric-based access control is receiving increasing attention due to its security and ease-of-use. However, concerns are often raised regarding the protection of the privacy of enrolled users. Signal processing in the encrypted domain has been proposed as a viable solution to protect biometric templates and the privacy of the users. In particular, several solutions have been proposed to protect the privacy of the biometric probe during the authentication process. In this paper we focus on privacy-preserving iris-based authentication. The main innovations compared to the prior art include: i) an iris masking technique that simplifies the operations on the encrypted data without sacrificing the recognition rate; ii) the adoption of a matching protocol based only on garbled circuits which offers longer term security over existing solutions based on homomorphic encryption or hybrid techniques. The computational and communication complexity of the on-line phase of the proposed protocol is extremely low, thus opening the way to its exploitation in practical applications.

The proliferation of digital cameras, wireless networks and distributed computing make sharing of visual data easier than ever. Such casual exchange of data, however, has increasingly raised questions on how sensitive visual information can be protected. Encrypted-domain signal processing techniques based on homomorphic encryption and garbled circuits are increasingly applied for such applications. Their high computation and communication complexity, however, are not suitable for pixel-level processing. In this paper, we propose an alternative approach of using information-theoretically secure protocols over multiple non-colluding semi-honest computing agents. The proposed protocols are based on classical Shamir's secret sharing scheme which supports multiplication and addition in the random-share domain. We extend the sharing scheme to handle other fundamental signal processing operations and use them to develop a novel privacy-protected wavelet denoising scheme over three computing agents. Our experimental results demonstrate the viability of using information-theoretic secure protocols to safeguard privacy in distributed pixel-level processing.

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to identify selected individuals and redact their images in the surveillance video. To identify individuals, the best known approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this paper, we propose an anonymous subject identification system based on homo-morphic encryption (HE). It matches the biometric signals in encrypted domain to provide anonymity to users. To make the HE-based protocols computationally scalable, we propose a complexity-privacy tradeoff called k-Anonymous Quantization (kAQ) which narrows the plaintext search to a small cell before running the intensive encrypted-domain processing within the cell. We validate a key assumption in kAQ that privacy is better preserved by grouping biometric patterns far apart into the same cell. We also improve the matching success rate by replacing the original bounding boxes with e-balls as basic units for grouping. Experimental results on a public iris biometric database demonstrate the validity of our framework.

In this paper, we consider the problem of incorporating privacy protection in a biometric access control system. An anonymous biometric access control (ABAC) system is proposed to verify the membership status of a user using biometric signals without knowing his or her true identity. This system is useful in protecting the privacy of authorized users while keeping out potential imposters and attackers. In our proposed system, we employ homomorphic encryption to protect the probe biometric and propose a secure similarity search (SSS) algorithm to authenticate the probe in an anonymous way. We demonstrate our proposed system using a gallery of iris biometrics and show the efficiency of the implemented anonymous biometric matching performed in encrypted domain.