In the wake of fraud scandals involving decentralized exchanges and the significant financial loss suffered by individuals, regulators are pressed to put mechanisms in place that enforce customer protections and capital requirements in decentralized ecosystems. Proof of liabilities (PoL) is such a mechanism: it allows a prover (e.g., an exchange) to prove its liability to a verifier (i.e., a customer).

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.

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 high-degree 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 well-studied assumptions.

Nowadays we are witnessing the democratization of cloud services. As a result, more and more end-users (individuals and businesses) are using these services for achieving their electronic transactions (shopping, administrative procedures, B2B transactions, etc.). In such scenarios, personal data is generally flowed between several entities and end-users need (i) to be aware of the management, processing, storage and retention of personal data, and (ii) to have necessary means to hold service providers accountable for the usage of their data. In fact, dealing with personal data raises several privacy and accountability issues that must be considered before to promote the use of cloud services. In this paper, we propose a framework for the representation of cloud accountability policies. Such policies offer to end-users a clear view of the privacy and accountability obligations asserted by the entities they interact with, as well as means to represent their preferences. This framework comes with two novel accountability policy languages. An abstract one devoted for the representation of preferences/obligations in an human readable fashion. And a concrete one for the mapping to concrete enforceable policies. We motivate our solution with concrete use case scenarios.

In this paper, we address the problem of privacy preserving delegated word search in the cloud. We consider a scenario where a data owner outsources its data to a cloud server and delegates the search capabilities to a set of third party users. In the face of semi-honest cloud servers, the data owner does not want to disclose any information about the outsourced data; yet it still wants to benefit from the highly parallel cloud environment. In addition, the data owner wants to ensure that delegating the search functionality to third parties does not allow these third parties to jeopardize the confidentiality of the outsourced data, neither does it prevent the data owner from efficiently revoking the access of these authorized parties. To these ends, we propose a word search protocol that builds upon techniques of keyed hash functions, oblivious pseudo-random functions and Cuckoo hashing to construct a searchable index for the outsourced data, and uses private information retrieval of short information to guarantee that word search queries do not reveal any information about the data to the cloud server. Moreover, we combine attribute-based encryption and oblivious pseudo-random functions to achieve an efficient revocation of authorized third parties. The proposed scheme is suitable for the cloud as it can be easily parallelized.

Counterfeit detection in RFID-based supply chains aims at preventing adversaries from injecting fake products that do not meet quality standards. This paper introduces CHECKER, a new protocol for counterfeit detection in RFID-based supply chains through on-site checking. While RFID-equipped products travel through the supply chain, RFID readers can verify product genuineness by checking the validity of the product's path. CHECKER uses a polynomial-based encoding to represent paths in the supply chain. Each tag T in CHECKER stores an IND-CCA encryption of T's identifier ID and a signature of ID using the polynomial encoding of T's path as secret key. CHECKER is provably secure and privacy preserving. An adversary can neither inject fake products into the supply chain nor trace products. Moreover, RFID tags in CHECKER can be cheap read/write only tags that do not perform any computation. Per tag, only 120 Bytes storage are required.

As RFID applications are entering our daily life, many new security and privacy challenges arise. However, current research in RFID security focuses mainly on simple authentication and privacy-preserving identification. In this paper, we discuss the possibility of widening the scope of RFID security and privacy by introducing a new application scenario. The suggested application consists of computing statistics on private properties of individuals stored in RFID tags. The main requirement is to compute global statistics while preserving the privacy of individual readings. PPS assures the privacy of properties stored in each tag through the combination of homomorphic encryption and aggregation at the readers. Re-encryption is used to prevent tracking of users. The readers scan tags and forward the aggregate of their encrypted readings to the back-end server. The back-end server then decrypts the aggregates it receives and updates the global statistics accordingly. PPS is provably privacy-preserving. Moreover, tags can be very simple as they are not required to perform any computation, but only to store data.

Publish/subscribe represents a new paradigm for distributed content delivery. It provides an alternative to address-based communication due to its ability to decouple communication between the source and the destination. However, it has remained a challenge to devise a scalable overlay supporting expressive content-filtering while satisfying the desirable requirements large distributed systems should fulfill. Our goal is to build an efficient P2P publish/subscribe network where only interested nodes are involved in event dissemination, and the amount of overhead generated by network discovery and membership management is small. In order to do so, we use a Bloom filter based mapping scheme to map IDs to nodes' interests, in addition to a new interest proximity metric to forward events and to build nodes' routing tables. As for network discovery we propose a new approach we call ldquoshared interest approachrdquo. Our scheme ensures an upper bound of routing tables size that only depends on the size of the ID digest. To evaluate the algorithms proposed in this work we conducted simulations in both static and dynamic settings.