Erik Pohle

The rapid increase of Internet of Things (IoT) systems across several domains has led to the generation of vast volumes of sensitive data, presenting significant challenges in terms of storage and data analytics. Cloud-assisted IoT solutions offer storage, scalability, and computational resources, but introduce new security and privacy risks that conventional trust-based approaches fail to adequately mitigate. To address these challenges, this paper presents MOZAIK, a novel end-to-end privacy-preserving confidential data storage and distributed processing architecture tailored for IoT-to-cloud scenarios. MOZAIK ensures that data remains encrypted throughout its lifecycle, including during transmission, storage, and processing. This is achieved by employing a cryptographic privacy-enhancing technology known as computing on encrypted data (COED). Two distinct COED techniques are explored, specifically secure multi-party computation (MPC) and fully homomorphic encryption (FHE). The paper includes a comprehensive analysis of the MOZAIK architecture, including a proof-of-concept implementation and performance evaluations. The evaluation results demonstrate the feasibility of the MOZAIK system and indicate the cost of an end-to-end privacy-preserving system compared to regular plaintext alternatives. All components of the MOZAIK platform are released as open-source software alongside this publication, with the aim of advancing secure and privacy-preserving data processing practices.

Garbling schemes are vital primitives for privacy-preserving protocols and secure two-party computation. This paper presents a projective garbling scheme that assigns 2 ⁿ values to wires in a circuit comprising XOR and unary projection gates. A generalization of FreeXOR allows the XOR of wires with 2 ⁿ values to be very efficient. We then analyze the performance of our scheme by evaluating substitution-permutation ciphers. Using our proposal, we measure high-speed evaluation of the ciphers with a moderately increased cost in garbling and bandwidth. Theoretical analysis suggests that for evaluating the nine examined ciphers, one can expect a 4- to 70-fold improvement in evaluation performance with, at most, a 4-fold increase in garbling cost and, at most, an 8-fold increase in communication cost compared to the Half-Gates (Zahur, Rosulek and Evans; Eurocrypt’15) and ThreeHalves (Rosulek and Roy; Crypto’21) garbling schemes. In an offline/online setting, such as secure function evaluation as a service, the circuit garbling and communication to the evaluator can proceed in the offline phase. Thus, our scheme offers a fast online phase. Furthermore, we present efficient Boolean circuits for the S-boxes of TWINE and Midori64 ciphers. To our knowledge, our formulas give the smallest number of AND gates for the S-boxes of these two ciphers.

IoT devices collect privacy-sensitive data, e.g., in smart grids or in medical devices, and send this data to cloud servers for further processing. In order to ensure confidentiality as well as authenticity of the sensor data in the untrusted cloud environment, we consider a transciphering scenario between embedded IoT devices and multiple cloud servers that perform secure multi-party computation (MPC). Concretely, the IoT devices encrypt their data with a lightweight symmetric cipher and send the ciphertext to the cloud servers. To obtain the secret shares of the cleartext message for further processing, the cloud servers engage in an MPC protocol to decrypt the ciphertext in a distributed manner. This way, the plaintext is never exposed to the individual servers. As an important building block in this scenario, we propose a new, provably secure family of lightweight modes for authenticated encryption with associated data (AEAD), called Eevee. The Eevee family has fully parallel decryption, making it suitable for MPC protocols for which the round complexity depends on the complexity of the function they compute. Further, our modes use the lightweight forkcipher primitive that offers fixed-length output expansion and a compact yet parallelizable internal structure. All Eevee members improve substantially over the few available state-of-the-art (SotA) MPC-friendly modes and other standard solutions. We benchmark the Eevee family on a microcontroller and in MPC. Our proposed mode Jolteon (when instantiated with ForkSkinny) provides 1.85x to 3.64x speedup in IoT-encryption time and 3x to 4.5x speedup in both MPC-decryption time and data for very short queries of 8 bytes and, 1.55x to 3.04x and 1.23x to 2.43x speedup, respectively, in MPC-decryption time and data for queries up to 500 bytes when compared against SotA MPC-friendly modes instantiated with SKINNY. We also provide two advanced modes, Umbreon and Espeon, that show a favorable performance-security trade-off with stronger security guarantees such as nonce-misuse security. Additionally, all Eevee members have full n-bit security (where n is the block size of the underlying primitive), use a single primitive and require smaller state and HW area when compared with the SotA modes under their original security settings.

The immense growth of data from the proliferation of Internet of Things (IoT) devices presents opportunities and challenges for privacy engineering. On the one hand, this data can be harnessed for personalized services, cost savings, and environmental benefits. On the other hand, (new) legislation must be complied with and privacy risks arise from collecting and processing of such data. Distributed privacy-preserving analytics offers a promising solution, providing insights while also protecting privacy. However, this approach has new challenges and risks, such as key management and confidentiality. When designing a data marketplace which offers distributed privacy-preserving analytics, the key management comes with different threats, which require a solution adapted to the distributed architecture.In this context, the paper presents a comprehensive, end-to-end secure system called MOZAIK for privacy-preserving data collection, analysis, and sharing. The article focuses on the key management aspect of the secure multi-party computation (MPC) component in a distributed privacy-preserving analytics architecture and the specific challenges created by introducing MPC. The proposed solution involves temporary storage of (symmetric) key shares and public-key encryption schemes to ensure secure key management for privacy-preserving computation. Our solution has the potential to be applied in other MPC-based setups, making it a valuable addition to the field of privacy engineering. By addressing key management challenges and risks, MOZAIK enhances data protection while enabling valuable insights from IoT data.