arXiv subjects

Fernando Beltran

Publications and source records attributed to Fernando Beltran.

3 recordsLinked to original sources

arXiv2026

Residual Privacy Budgeting with Weighted Scarcity Allocation for Online Query Answering

In many practical deployments of differential privacy, queries do not arrive all at once. We study online differentially private query answering under a finite zero-concentrated differential privacy (zCDP) contract. In this setting, queries arrive sequentially, carry different accuracy thresholds, and may overlap with information already released. We formulate this setting as residual privacy budgeting: for each arriving query, the mechanism first credits reusable support from previous DP outputs and then spends new budget only on the remaining support required to satisfy the current threshold. The controller separates feasible cases, where the minimal residual support is allocated exactly, from scarcity cases, where a weighted shortfall-conservation optimiser assigns limited support according to query difficulty. We define the weight using the Query Influence Factor (QIF), a diagnostic signal for query difficulty and instability rather than query importance. For scalar Gaussian exact reuse, inverse-variance fusion justifies additive support. We prove zCDP composition, residual minimality, 1-competitiveness against the offline optimum in the feasible regime, and avoidable expenditure for allocators that ignore released history. A scarcity impossibility result shows that no online allocator can guarantee a competitive ratio better than 1/n in threshold satisfaction, contextualising the QIF scarcity layer as a design choice for an inherently hard online problem.

cs.DB↗

arXiv2023

A Survey of Data Pricing for Data Marketplaces

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This paper attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.

cs.GT↗

arXiv2020

Selling Data at an Auction under Privacy Constraints

Private data query combines mechanism design with privacy protection to produce aggregated statistics from privately-owned data records. The problem arises in a data marketplace where data owners have personalised privacy requirements and private data valuations. We focus on the case when the data owners are single-minded, i.e., they are willing to release their data only if the data broker guarantees to meet their announced privacy requirements. For a data broker who wants to purchase data from such data owners, we propose the SingleMindedQuery (SMQ) mechanism, which uses a reverse auction to select data owners and determine compensations. SMQ satisfies interim incentive compatibility, individual rationality, and budget feasibility. Moreover, it uses purchased privacy expectation maximisation as a principle to produce accurate outputs for commonly-used queries such as counting, median and linear predictor. The effectiveness of our method is empirically validated by a series of experiments.

cs.GT↗