The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
Bradley A. Malin - One of the best experts on this subject based on the ideXlab platform.
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Privacy Enhancing Technologies - Integrating utility into face de-identification
Privacy Enhancing Technologies, 2006Co-Authors: Ralph Gross, Edoardo M. Airoldi, Bradley A. Malin, Latanya SweeneyAbstract:With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. However, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to preserve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between privacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy Protection Schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images.
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Protecting Genomic Sequence Anonymity with Generalization Lattices
Methods of information in medicine, 2005Co-Authors: Bradley A. MalinAbstract:Objectives: Current genomic privacy technologies assume the identity of genomic sequence data is protected if personal information, such as demographics, are obscured, removed, or encrypted. While demographic features can directly compromise an individual’s identity, recent research demonstrates such Protections are insufficient because sequence data itself is susceptible to re-identification. To counteract this problem, we introduce an algorithm for anonymizing a collection of person-specific DNA sequences. Methods: The technique is termed DNA lattice anonymization (DNALA), and is based upon the formal privacy Protection Schema of k -anonymity. Under this model, it is impossible to observe or learn features that distinguish one genetic sequence from k -1 other entries in a collection. To maximize information retained in protected sequences, we incorporate a concept generalization lattice to learn the distance between two residues in a single nucleotide region. The lattice provides the most similar generalized concept for two residues (e.g. adenine and guanine are both purines). Results: The method is tested and evaluated with several publicly available human population datasets ranging in size from 30 to 400 sequences. Our findings imply the anonymization Schema is feasible for the Protection of sequences privacy. Conclusions: The DNALA method is the first computational disclosure control technique for general DNA sequences. Given the computational nature of the method, guarantees of anonymity can be formally proven. There is room for improvement and validation, though this research provides the groundwork from which future researchers can construct genomics anonymization Schemas tailored to specific datasharing scenarios.
Xiao-yang Wang - One of the best experts on this subject based on the ideXlab platform.
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Research on Relay Protection Schema and Protection Setting of Large Capacity Synchronous Condenser
DEStech Transactions on Computer Science and Engineering, 2019Co-Authors: Xiao-yang WangAbstract:Now, the large capacity synchronous condensers are needed in the State Grid. However, the research on its Protection is rare. Based on its application in AC/DC converter stations, this paper summarizes the studies of the relay Protection function and Schema about large capacity synchronous condensers and transformer group, furthermore analyzes the main Protection principle and setting principle of relay Protection (differential Protection, inter-turn Protection, stator grounding Protection, etc). At last an example of setting value of synchronous condenser and transformer group Protection in converter station is given.
Latanya Sweeney - One of the best experts on this subject based on the ideXlab platform.
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Integrating Utility into Face De-Identification
2018Co-Authors: Ralph Gross, Edoardo Airoldi, Bradley Malin, Latanya SweeneyAbstract:With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. How- ever, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to pre- serve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between pri- vacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy Protection Schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images.
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Privacy Enhancing Technologies - Integrating utility into face de-identification
Privacy Enhancing Technologies, 2006Co-Authors: Ralph Gross, Edoardo M. Airoldi, Bradley A. Malin, Latanya SweeneyAbstract:With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. However, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to preserve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between privacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy Protection Schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images.
Tai Wang - One of the best experts on this subject based on the ideXlab platform.
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Unequal Error Protection Schema for Wireless H.264 Video Transmission Based on Perceived Motion Energy Model
2008 Second International Conference on Future Generation Communication and Networking Symposia, 2008Co-Authors: Gang Zhao, Gao Ming, Shuhui Wang, Tai WangAbstract:Unequal error Protection on video transmission is widely used to combat with bit errors in the wireless channel. However, current UEP Schemas are based on heuristic approaches and taking no account of the characteristics of human visual system. In this paper, a novel unequal error Protection Schema for wireless H.264 video transmission based on a modified perceived motion energy (PME) model is presented. According to the sensitive characteristics to video motions of human eyes, the proposed modified PME model taking account to the encoding features of H.264/AVC standard to analyze and model the motions in H.264/AVC encoded video. Based on this model, the video bitstream is divided into several quality layers and unequal error Protection is designed to protect the layered bitstream for the transmission over wireless channels. Experiment results show that higher video transmission quality is obtained.
Ralph Gross - One of the best experts on this subject based on the ideXlab platform.
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Integrating Utility into Face De-Identification
2018Co-Authors: Ralph Gross, Edoardo Airoldi, Bradley Malin, Latanya SweeneyAbstract:With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. How- ever, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to pre- serve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between pri- vacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy Protection Schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images.
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Privacy Enhancing Technologies - Integrating utility into face de-identification
Privacy Enhancing Technologies, 2006Co-Authors: Ralph Gross, Edoardo M. Airoldi, Bradley A. Malin, Latanya SweeneyAbstract:With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. However, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to preserve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between privacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy Protection Schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images.
