The Experts below are selected from a list of 21 Experts worldwide ranked by ideXlab platform
Viola Iva - One of the best experts on this subject based on the ideXlab platform.
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Homomorphic-Encrypted Volume Rendering
2020Co-Authors: Mazza Sebastia, Patel Daniel, Viola IvaAbstract:Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct Volume rendering approach that performs Volume rendering directly on Encrypted Volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the Volume data and rendered image are uninterpretable to the rendering server. Our Volume rendering pipeline introduces novel approaches for Encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their Volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote Volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.Comment: Accepted for presentation at IEEE VIS 202
Ivan Viola - One of the best experts on this subject based on the ideXlab platform.
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Homomorphic-Encrypted Volume Rendering
arXiv: Cryptography and Security, 2020Co-Authors: Sebastian Mazza, Daniel Patel, Ivan ViolaAbstract:Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct Volume rendering approach that performs Volume rendering directly on Encrypted Volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the Volume data and rendered image are uninterpretable to the rendering server. Our Volume rendering pipeline introduces novel approaches for Encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their Volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote Volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.
Mazza Sebastia - One of the best experts on this subject based on the ideXlab platform.
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Homomorphic-Encrypted Volume Rendering
2020Co-Authors: Mazza Sebastia, Patel Daniel, Viola IvaAbstract:Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct Volume rendering approach that performs Volume rendering directly on Encrypted Volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the Volume data and rendered image are uninterpretable to the rendering server. Our Volume rendering pipeline introduces novel approaches for Encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their Volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote Volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.Comment: Accepted for presentation at IEEE VIS 202
Sebastian Mazza - One of the best experts on this subject based on the ideXlab platform.
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Homomorphic-Encrypted Volume Rendering
arXiv: Cryptography and Security, 2020Co-Authors: Sebastian Mazza, Daniel Patel, Ivan ViolaAbstract:Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct Volume rendering approach that performs Volume rendering directly on Encrypted Volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the Volume data and rendered image are uninterpretable to the rendering server. Our Volume rendering pipeline introduces novel approaches for Encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their Volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote Volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.
Patrick Simmons - One of the best experts on this subject based on the ideXlab platform.
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security through amnesia a software based solution to the cold boot attack on disk encryption
Annual Computer Security Applications Conference, 2011Co-Authors: Patrick SimmonsAbstract:Disk encryption has become an important security measure for a multitude of clients, including governments, corporations, activists, security-conscious professionals, and privacy-conscious individuals. Unfortunately, recent research has discovered an effective side channel attack against any disk mounted by a running machine [23]. This attack, known as the cold boot attack, is effective against any mounted Volume using state-of-the-art disk encryption, is relatively simple to perform for an attacker with even rudimentary technical knowledge and training, and is applicable to exactly the scenario against which disk encryption is primarily supposed to defend: an adversary with physical access. While there has been some previous work in defending against this attack [27], the only currently available solution suffers from the twin problems of disabling access to the SSE registers and supporting only a single Encrypted Volume, hindering its usefulness for such common encryption scenarios as data and swap partitions Encrypted with different keys (the swap key being a randomly generated throw-away key). We present Loop-Amnesia, a kernel-based disk encryption mechanism implementing a novel technique to eliminate vulnerability to the cold boot attack. We contribute a novel technique for shielding multiple encryption keys from RAM and a mechanism for storing encryption keys inside the CPU that does not interfere with the use of SSE. We offer theoretical justification of Loop-Amnesia's invulnerability to the attack, verify that our implementation is not vulnerable in practice, and present measurements showing our impact on I/O accesses to the Encrypted disk is limited to a slowdown of approximately 2x. Loop-Amnesia is written for x86-64, but our technique is applicable to other register-based architectures. We base our work on loop-AES, a state-of-the-art open source disk encryption package for Linux.