Privacy-preserving Anonymization with Restricted Search (PARS) on Social Network Data for Criminal Investigations

Social network platforms have become the new norm for ensuring swift dissemination of information to a large audience. This has thus made these platforms a preferred choice of communication for many criminal organizations who tend to use them for their own iniquitous gains. These organizations take cover behind the user data/identity privacy policies, such as the General Data Protection Regulation (GDPR) [2] and Safe Harbor [4], which limit the Law Enforcement Agencies (LEAs) from accessing and analysing social media data without the consent of the users. Their veil is complemented by the fact that these malicious organization can operate from any part of the world and LEAs from different countries dither in sharing intelligence information among themselves. To overcome this issue, in this paper we propose a novel Privacy-preserving Anonymization with Restricted Search (PARS) approach which will provide LEAs with the leverage they need to access and analyse social media data without compromising individual privacy. We propose a new privacy concious node grouping approach that antagonizes relational information of a social network platform and we compliment this approach with the Public-key Encryption with Keyword Search (PEKS) mechanism that will enable LEAs to perform a restrictive search among each others' dataset without violating the privacy or leakage of the entire dataset to a third party. The proposed approach is applied on a Twitter dataset comprising of 277359 users that comment, re-tweet and/or like 11528 tweets and encryption and search times are evaluated. Furthermore, the proposed approach is tested for the effect of anonymization on information entropy of the twitter dataset.