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Decoding from Pooled Data: Sharp Information-Theoretic Bounds

2019
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SIAM Journal on Mathematics of Data Science
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Consider a population consisting of n individuals, each of whom has one of d types (e.g. their blood type, in which case d=4). We are allowed to query this database by specifying a subset of the population, and in response we observe a noiseless histogram (a d-dimensional vector of counts) of types of the pooled individuals. This measurement model arises in practical situations such as pooling of genetic data and may also be motivated by privacy considerations. We are interested in the number

doi:10.1137/18m1183339
dblp:journals/simods/AlaouiRKZJ19
fatcat:fcruanho7jgmvbryrbuiplejku