Papers tagged as DataAnalysis
  1. Homomorphic Proxy Re-Authenticators and Applications to Verifiable Multi-User Data Aggregation 2017 DataAnalysis FinancialCryptography fc17.ifca.ai
    David Derler, Sebastian Ramacher, Daniel Slamanig

    We introduce the notion of homomorphic proxy re-authenticators, a tool that adds security and verifiability guarantees to multi-user data aggregation scenarios. It allows distinct sources to authenticate their data under their own keys, and a proxy can transform these single signatures or message authentication codes (MACs) to a MAC under a receiver’s key without having access to it. In addition, the proxy can evaluate arithmetic circuits (functions) on the inputs so that the resulting MAC corresponds to the evaluation of the respective function. As the messages authenticated by the sources may represent sensitive information, we also consider hiding them from the proxy and other parties in the system, except from the receiver.


    We provide a general model and two modular constructions of our novel primitive, supporting the class of linear functions. On our way, we establish various novel building blocks. Most interestingly, we formally define the notion and present a construction of homomorphic proxy re-encryption, which may be of independent interest. The latter allows users to encrypt messages under their own public keys, and a proxy can re-encrypt them to a receiver’s public key (without knowing any secret key), while also being able to evaluate functions on the ciphertexts. The resulting re-encrypted ciphertext then holds an evaluation of the function on the input messages.

  2. MBeacon: Privacy-Preserving Beacons for DNA Methylation Data 2019 DataAnalysis Genomics NDSS Privacy ndss-symposium.org
    I. Hagestedt and Y. Zhang and M. Humbert and P. Berrang and H. Tang and X. Wang and M. Backes

    The advancement of molecular profiling techniques fuels biomedical research with a deluge of data. To facilitate data sharing, the Global Alliance for Genomics and Health established the Beacon system, a search engine designed to help researchers find datasets of interest. While the current Beacon system only supports genomic data, other types of biomedical data, such as DNA methylation, are also essential for advancing our understanding in the field. In this paper, we propose the first Beacon system for DNA methylation data sharing: MBeacon. As the current genomic Beacon is vulnerable to privacy attacks, such as membership inference, and DNA methylation data is highly sensitive, we take a privacy-by-design approach to construct MBeacon. First, we demonstrate the privacy threat, by proposing a membership inference attack tailored specifically to unprotected methylation Beacons. Our experimental results show that 100 queries are sufficient to achieve a successful attack with AUC (area under the ROC curve) above 0.9. To remedy this situation, we propose a novel differential privacy mechanism, namely SVT^2, which is the core component of MBeacon. Extensive experiments over multiple datasets show that SVT^2 can successfully mitigate membership privacy risks without significantly harming utility. We further implement a fully functional prototype of MBeacon which we make available to the research community.