1) the user’s privacy. The other advantage we

1)1) Non-Interactive Differential PrivacyThere are two ways by which we can implement the differential data privacy there are interactiveand non-interactive. In non-interactive settings we do not publish the data directly instead aninterface is built through which the users can query the data and there is no directly access. It isquite similar to k-Anonymity wherein we change the data before publishing it. Once the data ispublished the users request query to the database source which has statistical database through asecure interface like a firewall, then the query is processed and once the requested query result isobtained it is given back to the user. In non-interactive method we do not reveal the identity of theuser by which we can secure the user’s privacy. The other advantage we have in using noninteractivesetting is we can overcome the implementation delays which we face in the interactivesettings.

Therefore, it is preferred over interactive settings as it is even economical than theinteractive settings.Major Challenges in Non-Interactive Differential Privacy:There are certain drawbacks of differential privacy we will discuss them as follows:• When we publish the data, we will have to publish the entire data, if we want to only changethe certain data which has relevant information and publish small amount of data then wemight risk privacy and if we go ahead to publish entire data in case of sparse data theamount of data is really large and cost is very high.• In order to provide better utility and privacy the cost will go up. On the other hand, if wewant to publish data by only sampling small amount of data then we risk privacy leak. Thedata which might contain actual information is easily identified and might come underattack and get compromised.

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• The other issue we face is even if the security is effectively provided to our original datathe quality of the original data might get affected due to addition of excessive noise to theoriginal data. In other to provide complete security the quality of the original data tends todiminish.2)1)Privacy Issue’s: When we study regarding wearable devices there are certain security concernswhich surround them we are going to state them as follows:• From a customer point of view if I am using a device e.g.: Fitbit which tracks my dailyhealth activity I expect important information concerning my health to be given to mesafely without compromising my personal data.

These devices come with inbuilt globalpositioning tracking system which can track the current location of the user. If there is anyattack on the security system of the device the personal data of the user such as their currentlocation gets compromised.• On the other hand, the hospitals or the various health care institutions collect this data andanalyze it. The data should be securely stored and access to this data should be carefullygranted. As the value of this data is really high these health care institute face threats suchas hacking attacks or virus infection into their security systems.• Also, once the data is collected on the device it has to be transferred to the health careinstitutions via a secure channel. If we transfer the data through the Bluetooth then securityof the mobile might be at risk possibly a target to the hacker.• Usually the data on mobile or other devices is encrypted for safety but in case of wearabledevices there is no encryption of data there are mostly third-party application and somecompanies do not follow required security standards due to which customers personal datamight be under the risk.

• The other risk we face is of the signal interception suppose we have a wearable devicewhich is connected to our personal mobile through the network of any private organizationthen it becomes an easy job for the hacker to hack into the security system of the privateorganization due to known security flaw in our wearable device.Candidate Methods to Address the Privacy Issues:• Before we purchase any wearable device the best way to ensure privacy is by knowing thesecurity features of the device only if the device meet the security standards we can goahead with it also the manufacturer provides important security updates the customers needto follow them and update their devices in time.• The data collected by the health care organizations should be used only to treat and providegood medical attention to the customers. Also, the organizations should have rightinfrastructure to support the data which is collected also the network security should behigh such that any security failure are immediately reported.

• We need to establish a secure channel in order to transfer data such that it is securelytransferred to the storage system, because when we transfer the data there is a high risk ofit to be intercepted and leaked therefore a proper channel should be established. Also, it issuggested that the data can be transferred securely through the physical channel rather thanthe wireless medium. The transfer of data should be administered in order to prevent anyviolations.• The wearable device should follow design module which supports encryption so that thedata is encrypted and secured. These health care devices should always follow frameworkswhich provide encryption to the personal data.• If the signals from the wearable devices are picked up by any other source other that theauthorized organization then the security alarms must be in place to seize the transfer ofthe remaining information and secure the data.

Certain specific protocols should bedesigned to securely transfer the data such that the risk of interception by the hackers isovercome.References: Collection and Processing of Data from Wrist Wearable Devices inHeterogeneous and Multiple-User Scenarios.3)1)Benefits of Differential Privacy:In order to preserve the privacy if we first look into the k-anonymity, it is a technique wherewe de-identify or hide the data. In order to hide the data, we group them into similar types ofdata and publish large amount of data such that our original data is hidden.But simply hiding the data is not sufficient we still risk the sensitive information.

The hiddendata is still vulnerable in order to overcome this we also need to diverse the data. Therefore,we move on to the l-diversity approach which is similar to the previous technique but herethe data groups are well defined. It tries to achieve diversity of data by using minimumamount of data generalization. But in case we have data of high importance it is not enoughto only diverse it. We have to note that it is very important information which is prone toattack. Hence even if it overcomes the flaws of k-anonymity we still have not achieved theperfect privacy solution.

Taking note of these drawbacks, the differential privacy approachwas introduced which helps in overcoming the above flaws as well as provide better privacysolution to our data.In differential privacy the data related to an individual is protected by changing it before itgets published. It is stronger than the previously mentioned techniques and also can facethreats to the data. The concept is clear when the user request for information the originaldata is changed using a random algorithm and the result is delivered.

The output is nowhererelated to original data thereby providing security. Also, other advantage is it is not specificto any type of data, we can say that it is a general approach to provide security to our data.2)I have selected the following paper to critique:Reference:A Random Matrix Approach to Differential Privacy and Structure Preserved SocialNetwork Graph Publishing Faraz Ahmed, Rong Jin and Alex X.

Liu Department ofComputer Science and Engineering Michigan State University East Lansing, Michigan,USA {farazah, rongjin, alexliu}@cse.msu.eduBasic Idea:In this paper a technique is developed to provide privacy combining the concept of randommatrix with differential privacy. The attacks on social network platforms have motivated inthis direction.

The idea is to project the data as matrix and then perturb the matrix withrandom noise. Due to random projection the top eigenvectors are preserved. By randomprojection we can achieve differential privacy by small random perturbation. The main aim isto achieve the best tradeoff between privacy and tradeoff.Strength:• The technique is efficient as there is no data storage involved hence the data storage isnot an issue.• Along with good privacy the quality of the data is also preserved as we only introducesmall amount of random noise.• This is considered the best way to handle the large amount of data.• It is economical as it is cost efficient technique.Weakness:• We need a large network or large amount of data it works effectively only for the largerdata sets compared to smaller sets.• We require more memory utilization as the amount of data is large more memory isrequired.• The time taken for this technique is more as it is a longer process than a normaldifferential privacy procedure.• The data is treated as same even the sensitive data is treated like any ordinary data.


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