Talos a Platform for Processing Encrypted IoT Data

Shafagh, Hossein; Burkhalter, Lukas; Hithnawi, Anwar

doi:10.1145/2994551.2996536

Cited by 10 publications

(10 citation statements)

References 3 publications

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“…However, it suffered from high cost of decryption [50]. Paillier improved the earlier schemes by allowing encryption of plaintext data up to a bit-length , while reducing the ciphertext expansion during encryption and at the same time maintaining a reasonable encryption and decryption costs.…”

Section: Partially Homomorphic Encryption Schemesmentioning

confidence: 99%

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An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

Ramesh

Manimaran

2020

IEEE Internet Things J.

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I would like to dedicate this thesis to my familymy parents, Ramesh Rengaswamy and Vijay Ramesh who have always been the constant source of unconditional love, support and encouragement, my brother Dr. Sridharan Ramesh, for always being there whenever I needed moral support, motivation and guidance and to my partner Sudarsanan Krishnan, for the cheers, support and sacrifices throughout this journey. iii

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Section: Partially Homomorphic Encryption Schemesmentioning

confidence: 99%

“…In an IoT application, such an approach can potentially conflict with the security and privacy requirements. In addition, it has been identified that the solutions are computationally intensive with an overhead of 25% [50].…”

Section: Partially Homomorphic Encryption Schemesmentioning

confidence: 99%

An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

Ramesh

Manimaran

2020

IEEE Internet Things J.

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“…CryptDB is a secure DataBase Management System (DBMS) developed at MIT [12] with both academic [18], [20], [21], [47]- [53] and industrial [54]- [58] impacts [14], e.g., Google [54] and SAP [55] produced their own CryptDBinspired solutions. CryptDB aims at providing data privacy guarantees in the face of a compromised server and a honest but curious CSP by data encryption.…”

Section: B Cryptdbmentioning

confidence: 99%

Toward Securing Cloud-Based Data Analytics: A Discussion on Current Solutions and Open Issues

Moghadam¹,

Fayoumi²

2019

IEEE Access

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In the last few years, organizations and business professionals have realized the value of collaborative data analytics in supporting decision-making. Where several activities are performed on online data by different stakeholders, such as cleansing, aggregation, analysis, and visualization, cloud-based data analytics has become a favored choice for business professionals due to the elasticity, availability, scalability, and pay-as-you-go features offered by cloud computing. However, large amounts of data stored on the cloud are very sensitive (e.g., innovation, financial, legal, and customers' data), and so data privacy remains one of the top concerns for many reasons; mainly those relating to legal or competition issues. In this paper, we review the security and cryptographic mechanisms which aim to make data analytics secure in a cloud environment and discuss current research challenges.

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“…Hence, it can leak information if compromised. Talos [51,53] tailors CryptDB for IoT devices and eliminates the need to trust the application server. Mylar [47] introduces encrypted text file search with multiple keys.…”

Section: Related Workmentioning

confidence: 99%

“…The current PHE approaches are either targeted at single-key encrypted data [46,53,62] (no support for sharing) or consider only text-based data [27,47] (of limited use in an IoT context). Talos [51,53] is specifically tailored for IoT scenarios and has demonstrated PHE on embedded devices, but it does not offer any sharing features. Existing protocols for sharing, such as OAuth [35], fall short in providing strong assurances about the policy enforcements.…”

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confidence: 99%

Secure Sharing of Partially Homomorphic Encrypted IoT Data

Shafagh

Hithnawi

Burkhalter

et al. 2017

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

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IoT applications often utilize the cloud to store and provide ubiquitous access to collected data. This naturally facilitates data sharing with third-party services and other users, but bears privacy risks, due to data breaches or unauthorized trades with user data. To address these concerns, we present Pilatus, a data protection platform where the cloud stores only encrypted data, yet is still able to process certain queries (e.g., range, sum). More importantly, Pilatus features a novel encrypted data sharing scheme based on reencryption, with revocation capabilities and in situ key-update. Our solution includes a suite of novel techniques that enable efficient partially homomorphic encryption, decryption, and sharing. We present performance optimizations that render these cryptographic tools practical for mobile platforms. We implement a prototype of Pilatus and evaluate it thoroughly. Our optimizations achieve a performance gain within one order of magnitude compared to stateof-the-art realizations; mobile devices can decrypt hundreds of data points in a few hundred milliseconds. Moreover, we discuss practical considerations through two example mobile applications (Fitbit and Ava) that run Pilatus on real-world data.

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Talos a Platform for Processing Encrypted IoT Data

Cited by 10 publications

References 3 publications

An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

Toward Securing Cloud-Based Data Analytics: A Discussion on Current Solutions and Open Issues

Secure Sharing of Partially Homomorphic Encrypted IoT Data

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