## 1 Introduction
Such reviews are useful and relevant to developers and application store owners to better understand their customers and to recommend improvements [23; 24]. However, they are provided subjectively and descriptively, therefore harder exploitable for decision making whether or not to install the application. As for any social ecosystem, an application (seen as an agent) should be associated with a reputation score to predict bad behaviors [25]. Returning to Android, reputation can be evaluated based on permissions requests [26; 27; 28], Application Programming Interface (API) calls [8], information flow analysis [29; 30] and other features [9]. These approaches require prior installation and are based on app features. Unlike we would like to prevent installation through analysis of reviews. Interesting outcomesare obtained, revealing that reviews are useful to understand user sentiments in regard to Android products with the use of machine learning and deep learning techniques [31; 32; 33]. This understanding comes from efficient extraction and summarizing of key topics from reviews [24; 27; 34; 35; 36; 37], required to tell the developers exactly where to improve across updates [38; 39; 40; 41; 42; 43; 44; 23; 35; 36; 44]. The security aspect is a serious concern while updating. Concerning effectiveness of apps regarding the security point of view, literature provides with very few attempts. Mobile App Reviews Summarization (MARS) [45] and Security-Related Review Miner (SRR-Miner) [46] are two systems designed to summarize and mine security-related reviews to provide quick understanding to developers. Unlike evaluating security reputation, their aim is just to facilitate understanding and recommendations towards security. They are too coarse-grained since they deal with threat-related security keywords and the analysis is performed down to sentence-level. One with no knowledge about security would not be able to understand summaries and some knowledge is missing from the whole comment since only sentences with security-keywords are extracted. The last limitation is that authors do not consider negative sentiments, although the whole terms of comment are linked. Apart from SRR-Miner having a reliable dataset of keywords, the others are not consistent in selection of keywords. [PERSON] et al. [47] evaluated security-related reputation based on user feedback, the number of users who installed the studied application and the reputation of vendors. This proposal is subjective, does not exploit sentiment analysis on feedbacks and is subject to false reputation in case there are few feedbacks. Their interest focuses more on functional aspects than specifically on security. Like traditional Google star rating subject to inconsistencies across user perceptions, SERS [48] is a scheme proposed to rate applications regarding security claims. Authors rely on two opposed ways; the static analysis of required permissions leaking data and the sentiment analysis of reviews. However, it is unclear how they proceed with keywords and polarities and they only deal with the confidentiality aspect. Authors are not specific to reviews concerned with the whole reviews of apps. In light with all the aforementioned limitations, this work proposes, CIAA-RepDroid, a reputation scheme based on a review-level sentiment analysis of security-related reviews. CIAA-RepDroid exploits the probabilistic model Naive Bayes to estimate probability that the reputation of an app is likely good or bad. The specificity of the proposal is that we study more fine-grained aspects such as confidentiality, integrity, authentication and availability (CIAA) to provide enough ingredients to developers for improvement of future versions. More interestingly, with this system, a reputed app in term of security cannot be reputed in one of CIAA aspects. Experiments were conducted with 13 applications including live messaging, e-banking, social media and anti-malware including a total of 1050 security-related reviews and 7,835,322 functionality-related reviews.