Biblio
Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost – violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.
Several applications adopt electromagnetic sensors, that base their principle on the presence of magnets realized with specific magnetic materials that show a rather high remanence, but low coercivity. This work concerns the production, analysis and characterization of hybrid composite materials, with the use of metal powders, which aim to reach those specific properties. In order to obtain the best coercivity and remanence characteristics various "recipes" have been used with different percentages of soft and hard magnetic materials, bonded together by a plastic binder. The goal was to find out the interdependence between the magnetic powder composition and the characteristics of the final material. Soft magnetic material (special Fe powder) has been used to obtain a low coercivity value, while hard materials were primarily used for maintaining a good induction remanence; by increasing the soft proportion a higher magnetic permeability has been also obtained. All the selected materials have been characterized and then tested; in order to verify the validity of the proposed materials two practical tests have been performed. Special magnets have been realized for a comparison with original ones (AlNiCo and ferrite) for two experimental cases: the first is consisting in an encoder realized through a toothed wheel, the second regards the special system used for the electric guitars.