The analytical datasets should be protected for ensuring that the network traffic and business applications are secured (Dwork and Roth 2014). The following report outlines a brief discussion on the security, privacy and ethical issues in analytics with relevant details.
The sparse data refers to the data there are several gaps present within the data that is being recorded. These types of data come from sensors or any other non-information technology based devices. This sparse data almost goes in one way from sensor to the network. There are several devices that are required to be analysed under every condition (Cao et al. 2014). The new data scientist of the tech start-up has got the distinctive responsibility to understand the Netflix data challenge and various privacy issues that could be possible for the organization. Moreover, it was observed that he entire data challenge occurred due to sparse data and analytical dataset security.
There are some of the most significant technologies that are required to be considered while dealing with these privacy issues like disk encryption, software based mechanisms, backups, data erasure and many more (Zhu et al. 2015). The non information contextual data are being considered for anomaly detection logic. The technologies analyse and accumulate real time data, which involves asset metadata, threat intelligence and IP context. All of such forms of data could be utilized for both immediate investigations and threat response. The CTO of the organization is considering about the data threat or issue faced by Netflix. A high dimensional sparse dataset is responsible for analysing the sparse data and storing that data securely.
The most significant privacy vulnerabilities that are generally faced while securing sparse dataset are as follows:
i) Protection of Transaction Data and Logs: The first and the most important privacy vulnerability that is being faced while securing the sparse dataset is protection of transaction data and logs. Since, Netflix has to deal with several users’ data, it is extremely vital for them to protect their transactional data and logs. For this purpose one has to break the anonymity of the Netflix Prize Dataset (Uhlerop, Slavkovi? and Fienberg 2013). The anonymity can be broken after considering few significant steps and it is required to protect the dataset under every circumstance. When the data size is incremented, the availability and scalability makes auto tier important for the management of data storage. The storage location is required to be secured and hence Netflix data can be reduced.
ii) Validation and Filtration of End Point Inputs: Another important and noteworthy privacy and security issue that is required to be considered for sparse dataset anonymity, is the significant validation and filtration of the end point inputs (Chen et al. 2013). The end point devices are the major factors to maintain sparse dataset. The storage, processing as well as required works are being performed with an input data that is eventually provided for every end point. Hence, Netflix should ensure to utilize a legitimate and authenticated end point device. The validation and filtration of the end point inputs is required for understanding the large volumes of data and datasets (Lyu, Su and Li 2017). The reason for these types of breaches are the majority of security application, which is designed for storing certain amount of sparse data and the respective security technologies could become inefficient in managing the dynamic data.
iii) Securing Distributed Framework Calculations and Processes: The third type of distinct and subsequent privacy or security issue that is extremely vulnerable for the sparse dataset and Netflix anonymity is lack of securing a distributed framework calculations and any other process (Blum and Roth 2013). The computational security as well as the other digitalized assets within the distributed framework in Netflix prized dataset eventually lack any type of security protection. The two major preventions for this type of data security are protecting the dataset and securing the mappers within the presence of an unauthenticated and unauthorized mapper.
iv) Protection and Security of the Data in Real Time: The next significant and distinctive privacy and security issue that is required to be eradication under every circumstance is the lack of protection and security of real time data. For the large amount of this data generation, Netflix is often unable to preserve the regular data check. However, it is extremely beneficial to accomplish such type of security check as well as observations within the real time or even almost within real time.
v) Lack of Granular Access Control: This is again one of the major security issue that is to be considered for Netflix prized dataset. The granular access of the sparse data are stored by NoSQL databases with a mandatory access control and stronger authentication process. The lack of granular auditing is yet another significant issue that is required to be eradicated on time for the sparse data. Analysis of several types of logs can be beneficial and this data can be helpful for recognizing the type of malicious activities and cyber-attacks.
The anonymity of the sparse training data of Netflix could attacked if the above mentioned issues are not removed on time (Greenland, Mansournia and Altman 2016). Moreover, Netflix falls under the grouping of high dimensional sparse dataset and additional data of number of subscribers of Netflix is required. De-anonymization of the large sparse datasets is possible with database model, sparsity and similarity. After experiment, it is observed that this Netflix prize dataset is highly sparse and for these vast collection of records, there is not one single record with the subsequent similarity score more than 0.5 within the complete 500000 recorded dataset when the sets of movies are considered without considering the numerical ratings. Releasing and sanitation of data is required for implementing the changes.
The CTO of the tech start-up is thinking about bidding for a contract with the local government for the purpose of building an image recognition system for the law enforcement (Mason 2017). A face recognition system is the most distinct technology that has the capability to identify or verify an individual from a video frame or digital image. There are several distinct methods by which the facial recognition systems can work and the work is completed by comparing the selected facial features from a provided image with faces in the database. This is even termed as a biometric AI based application, which could easily and promptly recognize a person after analysing the patterns on the basis of an individual’s facial shape and textures (Slade and Prinsloo 2013). Since, CTO of his start-up has thought of bidding for a contract with the local government to build up this system, it is extremely important and significant to consider the ethical issues for such a contract.
The major and the most significant ethical issues that are required to be considered here are as follows:
i) Data Theft: This is the first and the most significant ethical issue that should be considered for bidding of this contract is theft of data. There are several chances of data hacking and the data could even be forged by the hackers. If the data is present within the cloud, indexed by Google, it is likely to get hacked by the hackers and data might lose confidentiality eventually (Mealer and Jones 2014). However, one cannot change someone’s face and there is an advantage as well that the cloud never forgets any face. Facial recognition system is also effective for solving crime for perpetuating social stigma, ethnic and racial profiling and many more.
ii) Terrorist Attacks: Since, the start-up will be starting a contract with the local government, it is required to consider the ethical issues faced by the government people. There is always a high chance the terrorist attacks should be considered and the facial recognition system would have a chance to identify the issues and complexities to a higher level (Miller and Blackler 2017). These types of attacks are extremely significant to identify the terrorists of a nation and a database of suspected criminals would have the ability to recognize the terrorists.
iii) Smart Closed Circuit Television: From the ethics based point of view, the smart closed circuit television is extremely interesting as it includes two distinct contested technologies of biometrics and video technology. FaceIt is the software engine, which is being run on a system for detecting and recognizing the human faces (Slade and Prinsloo 2013). It eventually undertakes human faces as the inputs after encoding them into digital images. This particular software is extremely ethical and has the capability of identifying the human faces without much complexities.
iv) Maintenance of Reasonable Data Security Protections: Another important and significant ethical issue that is required to be considered for the facial recognition system is that the chief technical officer of this start-up should consider the maintenance of reasonable data security protections (Martin 2015). For the consumer’s images, this type of protection is highly mandatory and they should store these images after putting protections in place and hence preventing any kind of unauthorized or unauthenticated scraping that leads to the unintended secondary utilizations.
v) Uses of Digital Sign: Another significant and noteworthy ethical issue that is required to be considered for the facial recognition system for this contract between government and the tech start-up is utilization of digital signs (Grinbaum and Groves 2013). This type of digitalized signs comprise of the significant capability to detect demography. The digital signs should give clearer notice to the clients that such technologies are in use, even before the clients are coming into contact with the respective signs. These digital signs can easily be used in the health care sector as it helps in receiving new sets of images of the various patients, who have any kind of genetic disorders. For maintenance of ethics and trust with the patients, the tech start-up must consider the involvement of relevant community stakeholders for the implementation of this facial recognition system and decisions for establishment and improvement of practices for informing patients regarding the utilization of this particular system of facial recognition system (Amos, Ludwiczuk and Satyanarayanan 2016). The detection of detecting a wide range of few behavioural conditions like development as well as behavioural disorders is highly require for maintaining ethical considerations within the organization.
vi) Identification of Anonymous Images: This is yet another important and significant ethical issue that is required to be considered under every circumstance. The organizations must not utilize the facial recognition system for the purpose of identification of anonymous images of one consumer to the next, who could not identify him or her without even obtaining an affirmative express content.
The ethical issues related to facial recognition system could not easily eradicated by following few steps. The first and the foremost step is to identify the issues effectively and efficiently. The regulatory issue or process issue is required to be considered eventually and hence code of ethics should be compared properly (Happy and Routray 2015). The major resources of this specific facial recognition system are required to be identified and information is to be updated effectively. A list of possible actions is made and the positive and negative consequences.
Conclusion
Therefore, it can be concluded that the CTO of the tech start-up has hired a new data scientist. He has read about Netflix data challenge and wanted to improve the organizational analytics. A high dimensional sparse dataset is explained in the above report and privacy issues are identified for that purpose. A contract is being made for building an image recognition system for law enforcement. The ethical issues are identified for this particular system.
References
Amos, B., Ludwiczuk, B. and Satyanarayanan, M., 2016. Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science, 6.
Blum, A. and Roth, A., 2013. Fast private data release algorithms for sparse queries. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (pp. 395-410). Springer, Berlin, Heidelberg.
Cao, N., Wang, C., Li, M., Ren, K. and Lou, W., 2014. Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Transactions on parallel and distributed systems, 25(1), pp.222-233.
Chen, R., Fung, B.C., Mohammed, N., Desai, B.C. and Wang, K., 2013. Privacy-preserving trajectory data publishing by local suppression. Information Sciences, 231, pp.83-97.
Dwork, C. and Roth, A., 2014. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), pp.211-407.
Greenland, S., Mansournia, M.A. and Altman, D.G., 2016. Sparse data bias: a problem hiding in plain sight. bmj, 352, p.i1981.
Grinbaum, A. and Groves, C., 2013. What is “responsible” about responsible innovation? Understanding the ethical issues. Responsible innovation: Managing the responsible emergence of science and innovation in society, pp.119-142.
Happy, S.L. and Routray, A., 2015. Automatic facial expression recognition using features of salient facial patches. IEEE transactions on Affective Computing, 6(1), pp.1-12.
Lyu, M., Su, D. and Li, N., 2017. Understanding the sparse vector technique for differential privacy. Proceedings of the VLDB Endowment, 10(6), pp.637-648.
Martin, K.E., 2015. Ethical issues in the big data industry. MIS Quarterly Executive, 14, p.2.
Mason, R.O., 2017. Four ethical issues of the information age. In Computer Ethics (pp. 41-48). Routledge.
Mealer, M. and Jones, J., 2014. Methodological and ethical issues related to qualitative telephone interviews on sensitive topics. Nurse Researcher, 21(4).
Miller, S. and Blackler, J., 2017. Ethical issues in policing. Routledge.
Slade, S. and Prinsloo, P., 2013. Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), pp.1510-1529.
Uhlerop, C., Slavkovi?, A. and Fienberg, S.E., 2013. Privacy-preserving data sharing for genome-wide association studies. The Journal of privacy and confidentiality, 5(1), p.137.
Zhu, T., Xiong, P., Li, G. and Zhou, W., 2015. Correlated differential privacy: Hiding information in non-iid data set. IEEE Transactions on Information Forensics and Security, 10(2), pp.229-242.
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