Biblio

Smart Meters (SM) are IoT end devices used to collect user utility consumption with limited processing power on the edge of the smart grid (SG). While SMs have great applications in providing data analysis to the utility provider and consumers, private user information can be inferred from SMs readings. For preserving user privacy, a number of methods were developed that use perturbation by adding noise to alter user load and hide consumer data. Most methods limit the amount of perturbation noise using differential privacy to preserve the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. Additionally, users may desire to select complete privacy without giving consent to having their data analyzed. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. The level of noise can be set to make user data differentially private preserving statistics or break differential privacy discarding the benefits of data analysis for more privacy. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman key exchange for symmetrical encryption of transferred data and a two-way challenge-response method for authentication.

We address the problem of ciphertext-policy attribute-based encryption with fine access control, a cryptographic primitive which has many concrete application scenarios such as Pay-TV, e-Health, Cloud Storage and so on. In this context we improve on previous LSSS based techniques by building on previous work of Hohenberger and Waters at PKC'13 and proposing a construction that achieves ciphertext size linear in the minimum between the size of the boolean access formula and the number of its clauses. Our construction also supports fast decryption. We also propose two interesting extensions: the first one aims at reducing storage and computation at the user side and is useful in the context of lightweight devices or devices using a cloud operator. The second proposes the use of multiple authorities to mitigate key escrow by the authority.

Delegating computation, which is applicable to many practical contexts such as cloud computing or pay-TV system, concerns the task where a computationally weak client wants to securely compute a very complex function f on a given input with the help of a remote computationally strong but untrusted server. The requirement is that the computation complexity of the client is much more efficient than that of f, ideally it should be in constant time or in NC0. This task has been investigated in several contexts such as instance hiding, randomized encoding, fully homomorphic encryption, garbling schemes, and verifiable scheme. In this work, we specifically consider the context where only the client has an input and gets an output, also called instance hiding. Concretely, we first give a survey of delegating computation, we then propose an efficient instance hiding scheme with passive input privacy. In our scheme, the computation complexity of the client is in NC0 and that of the server is exactly the same as the original function f. Regarding communication complexity, the client in our scheme just needs to transfer 4textbarftextbar + textbarxtextbar bits to the server, where textbarftextbar is the size of the circuit representing f and textbarxtextbar is the length of the input of f.