Data provenance is information used in reasoning about the present state of a data object, providing details such as the inputs used, transformations it underwent, entities responsible, and any other information that had an impact on its evolution. With a plethora of uses consisting of but not limited to provision of trust, gauging of quality, detecting intrusion and system changes, solving attribution problems, regulations compliance and in legal proceedings etc., provenance information needs to be secured. On the other hand use of tampered provenance information could lead to erroneous judgments and serious implications in many situations. The difference in sensitivity levels of provenance and the underlying data coupled with its DAG (Directed Acyclic Graph) structure leads to the need for a tailored security model. To date, proposed secure provenance schemes such as the Onion scheme, PKLC scheme, Mutual agreement scheme, rely on transitive trust; consecutive participating entities do not collude to attack the provenance chain. Furthermore, these schemes suffer from attacks such as ownership and lone attacks on provenance records. We propose a secure provenance scheme that uses the auditor as a witness to the chain build process whereby a verification tree is incrementally built by the auditor which serves as his view of the chain. Our scheme removes the transitive trust dependency hence collusion attacks by consecutive participating entities are successfully detected. Additionally, our scheme captures the DAG structure of provenance information and achieves secure provenance requirements; integrity, availability and confidentiality. Security analysis and empirical results show that the scheme provides better security guarantees than the previously proposed schemes with reasonable overheads involved that can be outweighed by the protection capabilities provided and removal of transitive trust which may not be feasible.
Computers & Security – Elsevier
Published: Mar 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera