Automatic Enforcement of Data Use Policies with DataLawyer

Abstract

Data has value and is increasingly being exchanged for commercial and research purposes. Data, however, is typically accompanied by terms of use, which limit how it can be used. To date, there are only a few, ad-hoc methods to enforce these terms. We propose DataLawyer, a new system to formally specify usage policies and check them automatically at query runtime in a relational database management system (DBMS). We develop a new model to specify policies compactly and precisely. We introduce novel algorithms to efficiently evaluate policies that can cut policy-checking overheads to only a few percent of the total query runtime. We implement DataLawyer and evaluate it on a real database from the health-care domain.

Achieving Data Truthfulness and Privacy Preservation in Data Markets

“…The following reasons account for this trend: 1) For the data contributors, they have severe privacy concerns [8]. Nevertheless, the service-based trading mode, which has hidden the sensitive raw data, alleviates their concerns; 2) For the service provider, comprehensive and insightful data services can bring in more profits [10]; 3) For the data consumers, data copyright infringement [11] is serious. However, such a data trading mode differs from most of conventional data sharing scenarios, e.g., data publishing [12].…”

Accomplishing Data Integrity and Confidentiality in Data Markets

“…In particular, the property of non-renouncement in traditional digital mark plans infers that the mark is remarkable", and any outsider is in a situation to check the genuineness of a data submitter utilizing her open key and consequently the relating digital certificate, i.e., the truthfulness of data combination in our model. Be that as it may, the check in digital mark plans needs the data of data, and may essentially presentation a data patron's genuine personality [12]. Identifying with MAC, the information donors and in this way the information customers should concur on a common mystery key that is unconventional in learning markets.…”

Raw Data Truthfulness and Privacy Preservation in a Practical Data Market

“…Interleaved comparison strategies [3]which compare rankers using click data, are a promising alternative to traditional information retrieval evaluation methods that require expensive explicit judgments. A major limitation of these methods is that they assume access to live data, meaning that new data must be collected for every pair of rankers compared [3]. 1) Disadvantage: It cannot support policies that require other semantics, such as creating a log entry when a violation occurs.…”

“…We present Account Trade, a set of accountable protocols for big data trading via data brokers. It enables data brokers to achieve trading-related accountability against dishonest consumers by blaming them when misbehaviour is detected [3]. [1] uses a scheme that integrates two cryptographic primitives, blind signature and hash chain, into a highly flexible and lightweight authentication and session key establishment protocol.…”

“…But the number of desirable security properties needed by this system are numerous and hence becomes complex. 1) Account Trade: Accountable Protocols for Big Data Trading Against Dishonest Consumers [3]uses a scheme that achieves the accountability against dishonest sellers who may re-sell others' datasets. This is a novel rigorous measurement of the dataset uniqueness which can be efficiently computable.…”