With the advent of cloud computing, individuals and companies alike are looking for opportunities to leverage cloud resources not only for storage but also for computation. Nevertheless, the reliance on the cloud to perform computation raises the unavoidable challenge of how to assure the correctness of the delegated computation. In this regard, we introduce two cryptographic protocols for publicly verifiable computation that allow a lightweight client to securely outsource to a cloud server the evaluation of highdegree univariate polynomials and the multiplication of large matrices. Similarly to existing work, our protocols follow the amortized verifiable computation approach. Furthermore, by exploiting the mathematical properties of polynomials and matrices, they are more efficient and give way to public delegatability. Finally, besides their efficiency, our protocols are provably secure under wellstudied assumptions.

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Abstract. RFID tags travel between partner sites in a supply chain.

Abstract. Existing work on data collection and analysis for aggregation is mainly focused on confidentiality issues. That is, the untrusted Aggregator learns only the aggregation result without divulging individual data inputs. In this paper we extend the existing models with stronger security requirements. Apart from the privacy requirements with respect to the individual inputs, we ask for unforgeability for the aggregate result. We first define the new security requirements of the model. We also instantiate a protocol for private and unforgeable aggregation for multiple independent users. I.e, multiple unsynchronized users owing to personal sensitive information without interacting with each other, contribute their values in a secure way: The Aggregator learns the result of a function without learning individual values, and moreover, it constructs a proof that is forwarded to a verifier that will convince the latter for the correctness of the computation. Our protocol is provably secure in the random oracle model.

Token payment systems were the first application of blockchain technology and are still the most widely used one. Early implementations of such systems, like Bitcoin or Ethereum, provide virtually no privacy beyond basic pseudonymity: all transactions are written in plain to the blockchain, which makes them linkable and traceable. Several more recent blockchain systems, such as Monero or Zerocash, implement improved levels of privacy. Most of these systems target the permissionless setting, and as such are not suited for enterprise networks. These require token systems to be permissioned and to bind tokens to user identities instead of pseudonymous addresses. They also require auditing functionalities in order to satisfy regulations such as AML/KYC. We present a privacy-preserving token management system for permissioned blockchains that also supports fine-grained auditing. The scheme is secure under computational assumptions in bilinear groups, in the random-oracle model. We provide performance measurements for our prototype built on top of Hyperledger Fabric.

Abstract. With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [8] recently suggested a solution that allows a centralized party to compute the sum of encrypted inputs collected through a smart metering network. The main shortcomings of this solution are its reliance on a trusted dealer for key distribution and the need for frequent key updates. In this paper we introduce a secure protocol for aggregation of timeseries data that is based on the Joye et al.[8] scheme and in which the main shortcomings of the latter, namely, the requirement for key updates and for the trusted dealer are eliminated. Moreover our scheme supports a dynamic group management, whereby as opposed to Joye et al.[8] leave and join operations do not trigger a key update at the users.

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