Functional encryption is a nice tool that bridges the gap between usability and privacy when providing access to huge databases: while being encrypted, aggregated information is available with a fine-tuned control by the owner of the database who can specify the functions he allows users to compute on the data. Unfortunately, giving access to several functions might leak too much information on the database, since once the decryption capability is given for a specific function, this is for an unlimited number of ciphertexts. In the particular case of the inner-product, if rows or records of the database contain ` fields on which one got ` independent inner-product capabilities, one can extract all the individual fields. On the other hand, the major applications that make use of inner-products, such as machine-learning, need to compute many of them. This paper deals with a practical trade-off in order to allow the computation of various inner-products, while still protecting the confidentiality of the data. To this aim, we introduce an oblivious helper, that will be required for any decryption-query, in order to control the leakage of information on the database. It should indeed learn just enough information to guarantee the confidentiality of the database, but without endangering the privacy of the queries.